{"product_id":"raspberry-pi-camera-trap-count-vehicles-using-opencv-kit","title":"Raspberry Pi Camera Trap: Count Vehicles Using OpenCV Kit","description":"\u003ch1\u003eWildlife Camera Trap Kit v26 - Count Vehicles with Edge AI and OpenCV\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5-6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 16-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Computer vision \u0026amp; data logging\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eTurn a Raspberry Pi 5 and Pi Camera Module 3 Wide into an autonomous wildlife camera trap that uses OpenCV background subtraction to detect and count vehicles crossing a virtual line. Log timestamp, count, and direction to CSV for later analysis. This project mimics real-world traffic monitoring tools used by conservationists and field researchers, bringing edge AI right to the trailhead or forest path.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou'll assemble a compact, weather-ready camera system that runs an OpenCV background subtraction algorithm on-device. A virtual line drawn in the camera's field of view triggers a counter whenever a vehicle-or large animal-crosses it. Directional data and timestamps are saved to a fast 128GB NVMe SSD, giving you days of unattended logging without network or cloud reliance. The kit is equally suited for wildlife studies and smart-city traffic projects.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eSet up a Raspberry Pi 5 with NVMe SSD for rapid boot and high-capacity data storage\u003c\/li\u003e\n  \u003cli\u003eCapture high-dynamic-range images with the Pi Camera Module 3 Wide in outdoor lighting\u003c\/li\u003e\n  \u003cli\u003eImplement background subtraction in OpenCV to detect moving objects and ignore static scenery\u003c\/li\u003e\n  \u003cli\u003eLog event data to CSV with precise timestamps and direction metadata using Python\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi Camera Module 3 Wide\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIdeal for B.Tech ECE\/EEE students building computer vision projects for Smart India Hackathon or final-year submissions, as well as ATL Tinkering Lab mentors guiding high-school innovators. Wildlife researchers and conservation volunteers will find it a reliable, low-cost alternative to commercial camera traps for vehicle or large-animal monitoring.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eOpen the AI companion from the QR code on your phone - it knows every line of code and every connection. If you prefer human help, message us on WhatsApp and we'll guide you.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this kit to monitor wildlife at night?\u003c\/summary\u003e\u003cp\u003eThe Pi Camera Module 3 Wide performs well in low light, but for total darkness you'll need an external IR illuminator (not included). The background subtraction algorithm works reliably if the scene is evenly lit.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need to know Python or OpenCV beforehand?\u003c\/summary\u003e\u003cp\u003eBasic Python familiarity is helpful, but the AI companion provides ready-to-run scripts and explains every step. You'll learn OpenCV fundamentals as you build and customise the project.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow many days of data can the 128GB SSD store?\u003c\/summary\u003e\u003cp\u003eThe NVMe SSD can hold several months of CSV logs and event-triggered still images. You can configure the system to only save frames when a crossing is detected, maximizing storage for long field deployments.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eWildlife - OpenCV background subtraction counts vehicles crossing a virtual line - logs count, timestamp and direction to CSV.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/4-channel-relay-board-for-esp32-30-pin-5v-control\"\u003ePi Camera Module 3 Wide\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Wildlife Camera Trap Kit v26?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Wildlife Camera Trap Kit v26 includes all components needed: Raspberry Pi 5 4GB, Pi Camera Module 3 Wide, NVMe SSD 128GB, Pi 5 M.2 HAT+, USB-C PSU and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Wildlife Camera Trap Kit v26?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 16-21. Wildlife - OpenCV background subtraction counts vehicles crossing a virtual line - logs count, timestamp and direction to CSV. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Wildlife Camera Trap Kit v26 online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Wildlife Camera Trap Kit v26 is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Wildlife Camera Trap Kit v26\",\n  \"description\": \"Wildlife - OpenCV background subtraction counts vehicles crossing a virtual line - logs count, timestamp and direction to CSV.\",\n  \"sku\": \"CDN-KIT-4227\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-wildlife-camera-trap-kit-v26\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"27845\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53463950524781,"sku":"CDN-KIT-4227","price":32860.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-wildlife-camera-trap-kit-v26.png?v=1781949860","url":"https:\/\/compoden.com\/products\/raspberry-pi-camera-trap-count-vehicles-using-opencv-kit","provider":"Compoden","version":"1.0","type":"link"}