With the shortcomings of traditional algorithm in video surveillance on low accuracy, poor robustness and unable achieved real-time tracking for multi-targets, this paper presents a Multi-target tracking algorithm, DeepSort, on the base of deep neural network to achieve the end-to-end surveillance video multi-personal target real-time detection and tracking. The high accuracy of target detection by YOLO algorithm provides DeepSort with weaker dependence on detection results, lower interference of occlusion and illumination and improved tracking robustness. Moreover, due to the high redundancy of the surveillance video itself, the difference filter is used to screen the video frames with no foreground targets and small changes, so as to reduce the detection cost and improve the detection and tracking speed. The experimental evaluation of the video surveillance dataset NPLR, the average MOTA of this algorithm is 68.7, the highest value is 86.8; the average speed is 81.6Hz, the highest value is 140Hz. It shows that the end-to-end algorithm is feasible and effective.
The packing presswork is an important factor of industrial product, especially for the luxury commodities such as cigarettes. In order to ensure the packing presswork to be qualified, the products should be inspected and unqualified one be picked out piece by piece with the vision-based inspection method, which has such advantages as no-touch inspection, high efficiency and automation. Vision-based inspection of packing presswork mainly consists of steps as image acquisition, image registration and defect inspection. The registration between inspected image and reference image is the foundation and premise of visual inspection. In order to realize rapid, reliable and accurate image registration, a registration method based on virtual orientation points is put forward. The precision of registration between inspected image and reference image can reach to sub pixels. Since defect is without fixed position, shape, size and color, three measures are taken to improve the inspection effect. Firstly, the concept of threshold template image is put forward to resolve the problem of variable threshold of intensity difference. Secondly, the color difference is calculated by comparing each pixel with the adjacent pixels of its correspondence on reference image to avoid false defect resulted from color registration error. Thirdly, the strategy of image pyramid is applied in the inspection algorithm to enhance the inspection efficiency. Experiments show that the related algorithm is effective to defect inspection and it takes 27.4 ms on average to inspect a piece of cigarette packing presswork.
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