Adaptive Load Balancing for Efficient Background Subtraction in Intelligent Transport Systems on Low-Cost Embedded Platforms

Background subtraction (BS) is a fundamental technique in intelligent transport vision systems, widely used to detect and track moving objects, such as vehicles, pedestrians and obstacles, in driving environments. It plays a crucial role in advanced driver-assistance systems (ADAS) and autonomous driving by enabling scene understanding and real-time motion analysis. However, BS processing must be optimized when targeting real-time processing on resource-constrained embedded systems, which present significant challenges due to limited computational power, memory constraints, and strict real-time requirements. Among the most commonly used BS techniques, the Codebook model and Gaussian Mixture Models (GMM) are known for their higher accuracy and light-model compared to many deep learning-based BS. In this work, we propose a fully heterogeneous CPU and GPU parallel implementation of both Codebook and GMM algorithms with an auto-load balancing over the processing units. This approach has been evaluated on the low-cost Jetson Orin Nano platform from NVIDIA, enabling efficient workload balancing across heterogeneous hardware resources. The suggested solution yields significant performance improvements over the state-of-the-art, achieving 59 frames per second (FPS) for GMM and 66 FPS for the Codebook method on full-HD (1080p) video streams. The results confirm the effectiveness of the proposed method in accelerating BS and demonstrate its suitability for real-time deployment in resource-constrained embedded environments.