JOURNAL ARTICLE

Object tracking using joint enhanced color-texture histogram

Abstract

Object tracking is an important task within the field of computer vision. There are two key steps in video analysis: detection of interesting moving objects and tracking of such objects from frame to frame. Mean shift object tracking algorithm is a feature based algorithm. In this paper color information of object are extracted as well as texture information is also extracted by using local binary pattern technique to represent the object. In joint color-texture histogram region of interest is extracted and then edges and corners are extracted from the region of interest. Color histogram is insensitive to nondeformation and partial occlusion but it does not perform well when in case of color aberration. To overcome this drawback Local Binary Pattern method is used. This feature makes this algorithm more efficient in comparison of color histogram algorithm.

Keywords:
Artificial intelligence Computer vision Color histogram Histogram Computer science Video tracking Pattern recognition (psychology) Local binary patterns Feature (linguistics) Color normalization Mean-shift Tracking (education) Object (grammar) Color image Image processing Image (mathematics)

Metrics

10
Cited By
0.00
FWCI (Field Weighted Citation Impact)
19
Refs
0.29
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Enhancement Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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