Build a real-time PPE detection pipeline using Qualcomm QIM SDK with daisy-chained ML models for person detection and protective equipment recognition — running entirely on-device with hardware-accelerated inference via Qualcomm HTP.
A four-stage sequential AI pipeline — palm detection, hand landmark estimation, gesture embedding, and gesture classification — built with Qualcomm QIM SDK for real-time edge deployment.
A real-time object detection pipeline built with QIM SDK using a YOLOv8 TensorFlow Lite model, supporting USB cameras, ISP cameras, RTSP streams, and video files — with RTSP/WebRTC output.
A scalable AI pipeline processing up to 31 input video streams in parallel with YOLOv8 object detection, compositing all streams into a single output and streaming over RTSP/WebRTC.
Real-time pipeline that identifies when a person steps into a predefined restricted zone using QIM SDK foot detection — with visual alerts, bounding box status indicators, and RTSP output.
A split-screen touchless driving game controlled entirely by wrist movement — built on a two-stage palm detection and hand landmark pipeline running on Qualcomm QIM SDK with QNN HTP acceleration.
A touchless virtual whiteboard controlled entirely by hand gestures — built on a two-stage palm detection and hand landmark pipeline running on Qualcomm QIM SDK with QNN HTP acceleration.
A YOLOv8-based object detection pipeline that counts products crossing a center region of interest in real time — built with Qualcomm QIM SDK and QNN HTP acceleration.