scholarly journals Object Tracking with Multiple Instance Learning and Gaussian Mixture Model

2015 ◽  
Vol 12 (11) ◽  
pp. 4465-4477
Author(s):  
Na Li
Keyword(s):  
Object Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Multiple Instance Learning

Modified Object Tracking and Counting Method Based on Gaussian Mixture Model

2013 ◽  
Vol 373-375 ◽  
pp. 598-602 ◽  
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang
Keyword(s):  
Object Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Counting Method ◽  
Centroid Distance ◽  
Monocular Video ◽  
Video Scene ◽  
Detection Ratio

In a monocular video scene, in order to improve the efficiency of object tracking and counting under occlusion conditions. The article presents a scheme to automatically track and count people in a surveillance system. First, a modified Gaussian mixture model was employed to determine pedestrian objects from a static scene. To identify foreground objects by positions and sizes of foreground regions which were obtained. Moreover, the performance to track objects was improved by using the modified overlap tracker, the modified overlap tracker was used to analyze the centroid distance between neighboring objects and help object tracking and people counting in occlusion states of merging and splitting. On the experiments of tracking and counting people in three video sequences, the results show that the proposed method can improve the averaged detection ratio about 10% as compared to the conventional work.

scholarly journals Road Surveillance using Gaussian Mixture Model for Birth and Clutter Events in Object Tracking

2012 ◽  
pp. 80-83
Author(s):  
V. Vijaya Chamundeeswari
Keyword(s):  
Object Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Contextual Information ◽  
Gaussian Mixture ◽  
Vehicle Tracking ◽  
Multiple Objects ◽  
Probability Hypothesis Density ◽  
Video Images ◽  
Target Environment

In video Surveillance for real time images, particularly, when applied for vehicle tracking in roads, complexity arises due to the fact that multiple objects or vehicles appera or disappear from the scene. The modeling of a road is a multi- target environment, where multiple targets are present in the scene. The appearance and disappearance of the targets are modelled by Gaussian Mixture Model (GMM). The model developed was used by Probability Hypothesis Density (PHD) filter. The PHD filter utilizes the contextual information so that occluded targets can be identified. The tracks for entered object, hidden and then appearing object can be extracted from the video images.

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