An Efficient Object Tracking on Edge Devices with Quantized Siamese Networks

Object tracking on embedded systems requires efficient models that operate within limited computational resources. Edge deployment reduces latency and power consumption, ensuring optimal performance for real-time applications in constrained environments. In this work, we propose a lightweight custom object tracker based on Siamese architecture, optimized for faster response and lower computation. We explore quantization-aware training (QAT) to reduce parameters in the backbone classifier. The tracker, validated on the VOT 2015 benchmark, shows minimal deviation compared to the baseline SiamFC. The tracker was implemented and tested across various platforms, including CPU, NVIDIA Jetson GPU, and PYNQ-Z2 FPGA, demonstrating efficient performance on resource-limited devices while maintaining real-time capabilities. Our compressed model is 2.1× smaller, with minimal accuracy loss, making it suitable for applications such as UAVs, traffic monitoring, real-time surveillance systems.