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摘要:
为了解决传统算法在全自动跟踪过程中遇到遮挡或运动速度过快时的目标丢失问题，本文提出一种基于YOLOv3和ASMS的目标跟踪算法。首先通过YOLOv3算法进行目标检测并确定跟踪的初始目标区域，然后基于ASMS算法进行跟踪，实时检测并判断目标跟踪效果，通过二次拟合定位和YOLOv3算法实现跟踪目标丢失后的重新定位。为了进一步提升算法运行效率，本文应用增量剪枝方法，对算法模型进行了压缩。通过与当前主流算法进行对比，实验结果表明，本算法能够很好地解决受到遮挡时跟踪目标的丢失问题，提高了目标检测和跟踪的精度，且具有计算复杂度低、耗时少，实时性高的优点。
Abstract:In order to solve the problem of loss when the target encounters occlusion or the speed is too fast during the automatic tracking process, a target tracking algorithm based on YOLOv3 and ASMS is proposed. Firstly, the target is detected by the YOLOv3 algorithm and the initial target area to be tracked is determined. After that, the ASMS algorithm is used for tracking. The tracking effect of the target is detected and judged in real time. Repositioning is achieved by quadratic fitting positioning and the YOLOv3 algorithm when the target is lost. Finally, in order to further improve the efficiency of the algorithm, the incremental pruning method is used to compress the algorithm model. Compared with the mainstream algorithms, experimental results show that the proposed algorithm can solve the lost problem when the tracking target is occluded, improving the accuracy of target detection and tracking. It also has advantages of low computational complexity, time-consuming, and high real-time performance.
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Key words:
- target tracking /
- target loss /
- you look only once v3 /
- model pruning /
- robust scale-adaptive mean-shift
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Overview: Tracking mobile objects has always been a challenging task and a hot research direction. Now, with the continuous improvement of hardware facilities and the rapid development of artificial intelligence technology, the technology of tracking mobile objects becomes more and more important. In order to solve the problem of loss when the target encounters occlusion or the speed is too fast during the automatic tracking process, this paper combines traditional algorithms with machine learning algorithms. As well as, a target tracking algorithm based on YOLOv3 and ASMS is proposed. Then, by pruning YOLOv3 and combining it with ASMS, the algorithm this paper proposed runs faster. The method of this paper first performs foreground detection through YOLOv3 to find the initial target area for tracking, which eliminats the need to manually circle the region of interest, and then performs tracking based on the ASMS algorithm. The algorithm based on YOLOv3 and ASMS detects and judges the tracking effect of the target in real time. When the tracking frame of ASMS is significantly offset from the detection target or the tracking frame is too large and contains too much background information, the tracking accuracy will decrease. If the target is blocked or moves too fast, it will be lost. For these two cases, YOLOv3 and quadratic fitting positioning are used to relocate to improve the accuracy of the algorithm and solve the problem of target loss. In order to further improve the efficiency of the algorithm, the method of incremental pruning is applied to compress YOLOv3. This article fine-tunes the network to reduce the reduction in algorithm accuracy caused by channel pruning and to prevent excessive pruning from causing network performance degradation. When performing model compression, firstly a scaling factor regular term is introduced for the sparse training of the convolutional layer channel of the YOLOv3 network. Then the global threshold is used to remove the components that are not important to the model reasoning, that is, the less scoring parts. An incremental pruning strategy is further used to prevent network degradation caused by excessive pruning. Finally, this paper fine-tunes the pruning model to compensate for potential temporary performance degradation. Compared with YOLOv3 in COCO database, the experimental results show that the speed of the best pruned algorithm is increased by 49.9%, the model parameters are reduced by 92.0%, and the body weight is reduced by 91.9%. After combining the pruned YOLOv3 with the ASMS algorithm, the experimental results show that the running speed of the proposed joint algorithm is 32.5% faster than the unpruned joint algorithm when the target has occlusion, and the accuracy is much better than that of ASMS. The proposed algorithm can solve the lost problem when the tracking target is occluded, improving the accuracy of target detection and tracking. Moreover, it has advantages of low computational complexity, time-consuming, and high real-time performance.
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图 2 通过稀疏训练和通道剪枝获得剪枝后的YOLOv3
Figure 2. YOLOv3 pruned through sparse training and channel pruning
图 3 基于YOLOv3和ASMS的跟踪算法流程图
Figure 3. The tracking algorithm flow chart based on YOLOv3 and ASMS
图 4 联合YOLOv3-95和ASMS算法的跟踪效果
Figure 4. The tracking performance of algorithm based on YOLOv3-95 and ASMS
图 5 传统ASMS算法的跟踪效果。(a) 行人；(b) 动物；(c) 小车
Figure 5. Tracking performance of the ASMS algorithm. (a) Pedestrian; (b) Animal; (c) Car
图 6 KCF算法跟踪效果。(a) 行人；(b) 动物；(c) 小车
Figure 6. Tracking performance of the KCF algorithm. (a) Pedestrian; (b) Animal; (c) Car
图 7 基于YOLOv3-95和ASMS算法的跟踪效果。(a) 行人；(b) 动物；(c) 小车
Figure 7. Tracking performance of the algorithm based on YOLOv3-95 and ASMS. (a) Pedestrian; (b) Animal; (c) Car
表 1 对比模型和剪枝模型评价结果
Table 1. Evaluation results of comparison model and pruning model
模型 精确度 mAP 速度/(f/s) 参数 体量 CPU GPU YOLOv3-tiny 32.7 24.1 48 120 8.9M 33.1MB YOLOv3 55.8 57.9 13 27 60.6M 231MB YOLOv3-50 57.6 56.6 22 48 19.8M 91.7MB YOLOv3-80 51.7 52.4 23 50 12.3M 46.6MB YOLOv3-95 49.4 46.5 27 57 4.8M 18.7MB 
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表 2 算法对比表
Table 2. Comparison among different algorithms
算法 平均巴氏距离 单帧平均耗时/s 传统ASMS算法 0.786 0.0098 KCF算法 0.795 0.0073 基于YOLOv3和ASMS算法 0.805 0.0631 基于YOLOv3-95和ASMS算法 0.803 0.0463
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表 3 算法对比表
Table 3. Comparison among different algorithms
算法 平均巴氏距离 单帧平均耗时/s 行人 动物 小车 行人 动物 小车 ASMS算法 0.3128 0.2564 0.3397 0.0093 0.0101 0.0104 KCF算法 0.3275 0.2631 0.3463 0.0078 0.0073 0.0085 基于YOLOv3和ASMS的算法 0.6965 0.6700 0.7201 0.0626 0.0611 0.0607 基于YOLOv3-95和ASMS的算法 0.6733 0.6574 0.7196 0.0469 0.0460 0.0473 VITAL算法 0.7043 0.6852 0.7253 1.6667 1.6823 1.6295 SANet算法 0.6965 0.6700 0.7201 1.3333 1.3478 1.3256
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