Adaptive Input Sampling: A Novel Approach for Efficient Object Detection in High Resolution Traffic Monitoring Images

Real-time traffic monitoring is crucial for efficient transportation management, but it poses significant computational challenges, particularly when dealing with high-resolution videos. Most modern traffic monitoring systems employ object detection models followed by object tracking models, which require processing every frame of the video footage to detect and track vehicles. However, even efficient object detection models are computationally expensive, making it difficult to process high-resolution videos in real-time. To address this challenge, we introduce an Adaptive Input Sampler (AIS) as a preprocessing module that selectively activates the object detector only when vehicles are present in the scene. The AIS targets the area of the frame where vehicles are detected and samples that area without compressing the sample, thereby reducing the computational load on the object detector. By doing so, the AIS enables significant computational savings, especially when fewor no vehicles are present in the monitored scene. Our experiments demonstrate that the AIS achieves 96.40% traffic counting accuracy and 95.60% classification accuracy, while reducing the data size for analysis by up to 79.00%. AIS presents a promising solution to optimize the volume of input data in traffic monitoring applications, improve computational efficiency, and speed up inference results.