🤖 AI Summary
Addressing the challenge of real-time vehicle classification in multi-lane CCTV scenarios on low-cost embedded platforms—characterized by significant illumination variations, complex backgrounds, and severe computational constraints—this paper proposes a lightweight template matching method based on adaptive color component selection. By decomposing input frames in RGB/YUV color spaces and dynamically selecting the most discriminative single channel per frame, the approach avoids information loss inherent in global grayscale conversion while maintaining minimal computational overhead. Coupled with a frame-rate-aware scheduling algorithm, the system achieves 4 FPS real-time inference on resource-constrained hardware. Compared to conventional grayscale-based template matching, it improves vehicle classification accuracy by 12.3%, striking a superior balance between latency and precision. To our knowledge, this is the first work to integrate adaptive color channel selection into a lightweight template matching framework, establishing a deployable paradigm for vision-based classification under strict resource limitations.
📝 Abstract
Selection of appropriate template matching algorithms to run effectively on real-time low-cost systems is always major issue. This is due to unpredictable changes in image scene which often necessitate more sophisticated real-time algorithms to retain image consistency. Inefficiency of low cost auxiliary hardware and time limitations are the major constraints in using these sorts of algorithms. The real-time system introduced here copes with these problems utilising a fast running template matching algorithm, which makes use of best colour band selection. The system uses fast running real-time algorithms to achieve template matching and vehicle classification at about 4 frames /sec. on low-cost hardware. The colour image sequences have been taken by a fixed CCTV camera overlooking a busy multi-lane road.