科研之友
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摘要
Recent digital acquisition systems can acquire high-resolution videos, generating a large amount of dynamic data and leading to higher computational cost in online target tracking and learning, especially for complex scenes. We introduce an efficient and robust approach to improve the performance of multi-object online tracking and learning. Prior methods saved on computational cost by scaling down each video frame to a fixed smaller resolution, without considering the image features. Our algorithm computes the optimal image resolution adaptively by exploiting the correlation between an image's gray-value distribution and resolution. This dimensionality reduction step significantly improves the time performance in subsequent online tracking and learning, while preserving high tracking accuracy. Since a small detection error in one frame can cause cumulative error in the video sequence leading to incorrect labeling and tracking, we introduce a new tracklet reliability assessment metric to eliminate incorrect samples. Experimental results show that our approach can successfully track multiple objects in real time with both high precision and recall.
