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Complex traffic scenes are susceptible to the occlusion of moving targets and blurred target images, resulting in incomplete extraction of target trajectories within the field of view (FOV). To address this issue, we proposed a complete trajectory extraction algorithm for moving targets (TraEA) that considers the multi-level semantic features of traffic scenes following the FOV of single-camera surveillance videos. TraEA utilizes semantic information from the multi-level scene structure, such as image pixel features, geometric features, as well as temporal and spatial relationships between targets within each keyframe image. The semantic information helps extract and optimize the complete trajectory of a target, to assemble the target’s moving trajectory with improved integrity. An experiment with real-world traffic videos collected in Beijing, China validated the proposed algorithm and showed that TraEA improved the completeness, accuracy and robustness of target trajectory extraction. Traffic trajectories from the experiment can facilitate traffic supervision by analyzing the moving behaviors of individual targets. While the proposed TraEA algorithm is applicable to extract trajectories from videos in other domains, such as animal movements, potential privacy infringement and abusive applications warrant cautions of ethical considerations.

Complete trajectory extraction for moving targets in traffic scenes that considers multi-level semantic features