|
Multiple vehicles tracking in the aerial video have a strong effect on the intelligent transportation system. Therefore, a fast multiple vehicles tracking method is required to meet the demand of a higher energy conversion efficiency. However, frequent and long-term occlusions in complex traffic scenes make it more difficult, and identity switches of vehicles with a similar appearance also bring challenges. Currently, vehicles tracking methods based on detection shows satisfactory performance. In the tracking-after-detection methods, data association plays the key role, and it includes two types, namely, frame-by-frame association and multi-frame association. Frame-by-frame association refers to the association between detections in the two consecutive frames, but tracking drift or failure is likely to occur when vehicles are blocked or not detected. The multi-frame association establishes a relational model by using object detection information of multiple frames instead of the previous two frames, which can effectively reduce the vehicle error association and deal with occlusions. However, the tracking will also be interrupted if the occlusion time is too long to associate the detection points before and after. Therefore, online multiple vehicles tracking in the aerial video based on fast incremental discriminative appearance learning is put forward. A fast incremental discriminative appearance learning (FIDAL) is introduced to discriminate the appearances of vehicles and adaptively update the vehicle appearance models based on the difference value between the new sample and the mean of vehicle samples to address the problem of identity switches. Experimental results on video sequences from different data sets demonstrate an average 25 percent performance improvement when using fast incremental discriminative appearance learning.
|
