Towards Real-Time Object Detection and Tracking at the Edge

Real-time object detection, recognition and tracking is essential for safety critical applications such as autonomous vehicles as well as video analytics, where critical information should be extracted from video streams for applications such as surveillance for security, health and safety monitoring in healthcare and industry, intelligent transportation systems and smart cities. To reduce the latency and security vulnerabilities, processing at the edge devices is critical. However, algorithms usually used in these applications to achieve the required level of accuracy are very computationally intensive. In this talk, two case studies are discussed to present the challenges and techniques of real-time object detection and tracking. First, we’ll present a hardware/software co-design approach for two critical tasks, real-time pedestrian detection, and vehicle detection, which are essential in advanced driving assistance systems (ADAS) and autonomous driving systems (ADS). In the second part of this talk, an initial work on person re-identification and tracking is presented focusing on challenges for the processing at the edge.

Speaker: Morteza Biglari-Abhari (University of Aukland, New Zealand )