Several studies have shown that cyclists can reduce the risk of severe head injuries by wearing a helmet. A system is proposed to collect cyclist helmet usage data automatically from video footage. Computer vision techniques are used to track the moving objects and then to analyze the object trajectories and speed profiles to identify cyclists. Image features are extracted from a region around the cyclist's head. Support vector machines determine whether the cyclist is wearing a helmet. The system can be approximately 90% accurate in cyclist classification when provided with accurate tracks of the cyclist's head. Even for situations in which obtaining video to track a cyclist is challenging, the proposed method provides an effective retrieval system, potentially reducing the number of video records that must be analyzed manually to find instances of cyclists not wearing helmets.
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