@surveillance-bot

surveillance-bot

README.md

Built during Chandigarh Police - Infosys Hackathon

October 2022

Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.

ML Project - December 2022

Project Title:-

Integration of CCTV Feeds using Various Cameras

Tech Stack:-

  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask

Project Flow:-

  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.

Project Outcome:-

  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

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  1. flask-backendflask-backendPublic

    Python 1

  2. frontendfrontendPublic

    HTML

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

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1

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    HTML
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    Jupyter Notebook

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

Pinned
Loading
  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

Repositories

Showing 6 of 6 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

Pinned
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  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

Repositories

Showing 6 of 6 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

Pinned
Loading
  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

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Showing 6 of 6 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

Pinned
Loading
  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

Repositories

Showing 6 of 6 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

Pinned
Loading
  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

Repositories

Showing 6 of 6 repositories
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
@surveillance-bot

surveillance-bot

README.md
Built during Chandigarh Police - Infosys Hackathon
October 2022
Our project called ChanPol (derived from InterPol) smoothens the process of collecting evidences against a crime, given the facial identity of suspect is known. It can collect important intel such as the location tracking, location history and course of suspect's actions to monitor them by aiming at face extraction and detection applied onto the video footage of 2,000+ pre-installed CCTVs spreaded over 114 km2 area to capture various angles and views.
ML Project - December 2022
Project Title:-
Integration of CCTV Feeds using Various Cameras
Tech Stack:-
  • Python
  • OpenCV (Haarcascade classifiers, Deepface)
  • Flask
Project Flow:-
  • We have 'N' no. of CCTV footages of different locations provided with us.
  • Using Face Detection, our system finds all the faces present in any video.
  • Then using Face Extraction, cropped screenshots of the faces are extracted from the video.
  • A PDF is then auto-generated which consists of the faces of the persons seen in the video along with statistics such as the no. of occurrences of that face in the video & the particular timestamps when that face has occurred in the video.
  • Then a particular suspect/criminal image is provided to us.
  • We search for that particular image on all of the CCTV footages that we have.
  • A PDF is then auto-generated which consists of a report that the suspect's face has been identified on which videos along with the above mentioned statistics.
Project Outcome:-
  • Given any suspect image, we can search and conclude from the generated report that where that suspect has been found.
  • From the conclusions, we can visualise and generate a possible route of the suspect and probably find the current location of the suspect.
  • From the first report that we generated, in which all the identified faces in all the videos were found, we can use that unused data in any future case.

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  1. flask-backendflask-backendPublic
    Python
1

  2. frontendfrontendPublic
    HTML
  3. cnn_facial_recocnn_facial_recoPublic
    Jupyter Notebook

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