Abstract
Multi-target pig tracking algorithm based on joint probability data association and particle filter
Highlights
Multi-target tracking in real scenes is becoming more and more important in computer vision and image processing applications[1,2,3], such as intelligent surveillance and control, robotics, space navigation, and automatic driving
In view of the limitations of existing pig individual behavior monitoring technology, and based on the pig behavior characteristics and moving target tracking technology, this paper proposes a method that uses the color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter
The joint probabilistic data association (JPDA) algorithm was used in this study, which is an extension of the PDA algorithm for a single target[20]
Summary
Multi-target tracking in real scenes is becoming more and more important in computer vision and image processing applications[1,2,3], such as intelligent surveillance and control, robotics, space navigation, and automatic driving. With the increasing number of tracking targets, how to solve complex interactions and occlusions becomes a very difficult and important issue in visual tracking[4]. It has been believed a crucial and challenging problem since there are many uncertain factors during multi-target tracking[5], such as measurement noise, cluttered background, occlusions, the changeable number of targets, and varying targets’ appearances and motions. The limitations imposed by the measurement were taken into account, the estimation process has to consider the noise associated with them to obtain reliable information about the position
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