Improved moving object tracking approach based on Mean-Shift in complex industrial situations

With the rapid development of the computing technology in recent years, detecting and tracking moving target in video has become a problem urgently to be solved in computer vision. Mean-Shift tracking algorithm has become one of the most commonly used algorithms for its excellent robustness and processing speed. Because of the change of the position against camera, the light in the warehouse, the noise, and the similar objects around, the efficiency, the accuracy and the anti-jamming capability of the traditional Mean-Shift tracking algorithm are not able to meet the requirement of the application of moving boxes in the logistical storages. In order to solve these problems, a series of improved solutions was put forward such as histogram optimization, histogram update and moving information amalgamation which could improve the efficiency, the accuracy and the anti-jamming capability. The moving vector weighing scheme could improve the discriminative capability of the algorithm and make the tracking more successful. Simulation experiments on some test videos validate that the proposed approach can meet the requirements of the targeted industrial application.