基于改进DSST的行人遮挡跟踪算法

为解决判别尺度空间跟踪(DSST)算法在行人处于长期完全遮挡后又重新出现的情况下无法跟踪的问题, 提出了一种改进的跟踪算法(DDSST). 在DSST框架下,首先对行人目标跟踪, 然后, 引入高置信度指标计算策略作为跟踪准确可信度反馈机制, 在跟踪丢失时利用可变部件模型(DPM)对跟踪目标重新定位. 最后, 通过评估在线目标跟踪基准(OTB)数据集和实际环境拍摄的视频序列对DDSST算法准确性进行验证, 并与其他跟踪算法进行比较. 实验分析表明, 改进算法相较DSST的距离精度与成功率提高了4.1% 和6%, 相比其他算法性能更优, 且在形变、遮挡、平面外旋转、运动模糊和尺度变换等条件下跟踪更稳定.

Pedestrian Occlusion Tracking Algorithm Based on Improved DSST

In order to solve the problem that the discriminative scale space tracking (DSST) algorithm cannot track when pedestrians reappear after being completely occluded for a long time, an improved tracking algorithm (DDSST) is proposed. Under the DSST framework, pedestrian tracking is first performed. And then the high confidence index calculation strategy is introduced as the tracking accuracy credibility feedback mechanism. when the tracking is lost, the deformable part model (DPM) is used to relocate the tracking target. Finally, the accuracy of the DDSST algorithm is verified by evaluating the online Object target Tracking Benchmark (OTB) dataset and the video sequences captured in the actual environment, and compared with other tracking algorithms. Experimental analysis shows that the distance precision and success rate of the improved algorithm are improved by 4.1% and 6% compared with DSST, and the performance is better than other algorithms, and the tracking performed under conditions such as deformation, occlusion, out-of-plane rotation, motion blur, and scale transformation is more stable.