{"product_id":"kit-retail-footfall-camera-pro-kit-with-raspberry-pi-5-plus-camera","title":"Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera","description":"\u003ch1\u003eBuild a Retail Footfall Camera That Runs AI at 13 TOPS — Raspberry Pi 5 + Hailo-8L Kit\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 4-5 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 Edge AI deployment \u0026amp; benchmarking\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eImagine a camera that not only watches a store entrance but instantly classifies every person crossing the threshold — without sending a single frame to the cloud. This kit lets you build exactly that. You will mount a Hailo-8L AI accelerator onto a Raspberry Pi 5, connect the Camera Module 3, and deploy a MobileNetV2 image classification model to track footfall in real time. The real challenge is not just assembling hardware; it is running an inference benchmark and comparing the blazing speed of the Hailo-8L at 13 TOPS against the Pi 5’s CPU-only performance, a hands-on lesson in edge computing that transforms how you think about AI at the point of capture.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou will create a self-contained edge AI camera that captures live video, classifies each frame as containing a person or not, and logs timestamped counts to the onboard NVMe SSD. The system runs entirely on-device, making it a miniaturised version of the people-counting analytics used in modern retail. By the end of the build, you will have a working footfall monitor that you can benchmark, tweak, and even extend to recognise other objects.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy a pre-trained MobileNetV2 model on the Hailo-8L neural processor and measure its throughput in frames per second\u003c\/li\u003e\n  \u003cli\u003eCompare inference performance between the Hailo accelerator and the Raspberry Pi 5’s Arm CPU using identical input streams\u003c\/li\u003e\n  \u003cli\u003eStream and process camera data with OpenCV and the HailoRT runtime on Raspberry Pi OS\u003c\/li\u003e\n  \u003cli\u003eDesign a real-world edge AI pipeline that captures, classifies, logs, and retrieves footfall metrics without cloud dependency\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\u003eRaspberry Pi AI HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi Camera Module 3\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\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\u003eIf you are a B.Tech ECE or CSE student exploring embedded vision for a Smart India Hackathon project, or a CBSE Class 12 student preparing a computer science investigatory project, this kit gives you a complete edge AI prototyping platform. It also suits ATL Tinkering Lab mentors who want to demonstrate real-time neural network inference on low-power devices. The build complexity aligns well with first-year engineering workshops at IITs, NITs, and VIT.\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\u003eScan the QR code on the box to launch the AI companion; it knows every step of the retail footfall camera project. You can also message us on WhatsApp and we’ll help you within hours.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I run a different model on the Hailo-8L?\u003c\/summary\u003e\u003cp\u003eYes. The Hailo-8L supports many classification and detection models from the Hailo Model Zoo. Our companion guides you through compiling and deploying a custom model, so you can switch from MobileNetV2 to YOLO or other networks.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs the NVMe SSD essential for this project?\u003c\/summary\u003e\u003cp\u003eThe 128 GB NVMe SSD provides fast local storage for high-resolution video logs and model data, and boots the Pi 5 significantly faster than a microSD card, which is critical when you run hours-long footfall benchmarks.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow do I see the benchmark comparison between CPU and Hailo?\u003c\/summary\u003e\u003cp\u003eThe build companion includes a benchmarking script that runs MobileNetV2 inference on the Pi 5’s CPU and then on the Hailo-8L, displaying side-by-side frames per second and latency. You will generate a performance report you can include in your project documentation.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eRetail Analytics — Hailo-8L on Pi AI HAT+ runs MobileNetV2 image classification at 13 TOPS — benchmark inference vs CPU on Pi 5.\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\/industrial-ph-sensor-module-for-arduino-esp32-raspberry-pi\"\u003eRaspberry Pi AI HAT+\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\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-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 Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera includes all components needed: Raspberry Pi 5 4GB, Raspberry Pi AI HAT+, Pi Camera Module 3, NVMe SSD 128GB, 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 Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 16-21. Retail Analytics — Hailo-8L on Pi AI HAT+ runs MobileNetV2 image classification at 13 TOPS — benchmark inference vs CPU on Pi 5. Estimated build time is 4-5 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Retail Footfall Camera Pro Kit with Raspberry Pi 5 + Camera is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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