Haar-Like and HOG Fusion Based Object Tracking

  • Chong Xia
  • Shui-Fa Sun
  • Peng Chen
  • Heng Luo
  • Fang-Min Dong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)


Only unitary feature for object is adopted in the conventional tracking system, making it difficult for robust tracking. Regarding the characteristic of both Haar-like and HOG features, a tracking algorithm fusing these two features is proposed: using the Haar-like features for the structure of the object and HOG features for the edge. A mixed feature pool is constructed with these two features. The On-line Boosting feature selection framework is adopted to select out the notable features, and update these features on line to realize the optimal selection. Four representative videos are used to test the performance of the proposed algorithm in the aspect of illumination change, tacking small targets, complex motion of the object, similar object interference during tracking and so on. Statistical analysis Results of the error show that the tracking system using the fused features outperforms the system using either of the two features.


Keywords: Object Tracking On-line Boosting Haar-like HOG fusion 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chong Xia
    • 1
  • Shui-Fa Sun
    • 1
  • Peng Chen
    • 1
    • 2
  • Heng Luo
    • 1
  • Fang-Min Dong
    • 1
    • 2
  1. 1.Institute of Intelligent Vision and Image InformationChina Three Gorges UniversityYiChangChina
  2. 2.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric EngineeringChina Three Gorges UniversityYichangChina

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