Abstract
Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.
Highlights
Object tracking [1, 2] is an important research field in computer vision for its wide range of application demands and prospects in industries, such as intelligent human-computer interaction, video monitoring, and intelligent transportation
For video object tracking study, there are mainly three methods: (1) method based on pattern matching, (2) method based on classification, and (3) method based on object state estimation
Mean Shift [5, 6] is the most typical object pattern matching algorithm. This method has relatively small calculating amount and can achieve fast pedestrian detection and tracking in static background. It is difficult for pedestrian detection and tracking in moving background, which limits the application range of this method; method based on classification [7,8,9] transforms object tracking into foreground and background classification and usually adopts machine learning method for processing
Summary
Object tracking [1, 2] is an important research field in computer vision for its wide range of application demands and prospects in industries, such as intelligent human-computer interaction, video monitoring, and intelligent transportation. Mean Shift [5, 6] is the most typical object pattern matching algorithm This method has relatively small calculating amount and can achieve fast pedestrian detection and tracking in static background. It is difficult for pedestrian detection and tracking in moving background, which limits the application range of this method; method based on classification [7,8,9] transforms object tracking into foreground and background classification and usually adopts machine learning method for processing. Due to particle filters’ non-Gaussian, nonlinear assumption and multiple hypothesis property, it has been successfully applied to video object tracking [11]. It becomes the mainstream research method [12]
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