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Advances in Image and Video Technology

Volume 4872 of the series Lecture Notes in Computer Science pp 678-691

Detection and Classification of Human Movements in Video Scenes

  • A. G. HochuliAffiliated withPostgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155 Prado Velho, 80215-901, Curitiba, PR
  • , L. E. S. OliveiraAffiliated withPostgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155 Prado Velho, 80215-901, Curitiba, PR
  • , A. S. BrittoJr.Affiliated withPostgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155 Prado Velho, 80215-901, Curitiba, PR
  • , A. L. KoerichAffiliated withPostgraduate Program in Computer Science (PPGIa), Pontifical Catholic University of Parana (PUCPR), R. Imaculada Conceição, 1155 Prado Velho, 80215-901, Curitiba, PR

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Abstract

A novel approach for the detection and classification of human movements in videos scenes is presented in this paper. It consists in detecting, segmenting and tracking foreground objects in video scenes to further classify their movements as conventional or non-conventional. From each tracked object in the scene, features such as position, speed, changes in direction and temporal consistency of the bounding box dimension are extracted. These features make up feature vectors that are stored together with labels that categorize the movement and which are assigned by human supervisors. At the classification step, an instance-based learning algorithm is used to classify the object movement as conventional or non-conventional. For this aim, feature vectors computed from objects in motion are matched against reference feature vectors previously labeled. Experimental results on video clips from two different databases (Parking Lot and CAVIAR) have shown that the proposed approach is able to detect non-conventional human movements in video scenes with accuracies between 77% and 82%.

Keywords

Human Movement Classification Computer Vision Security