Abstract
Gait recognition has become an active research area with increasing demand for effective video surveillance systems. This paper deals with an innovative method of modelling human gait with spline curves. The method proposed involves finding the locations of several human joints namely, coxa joint, a pair of knee joints and a pair of ankle joints. The five joints located are used as control points to construct spline curve. Instead of comparing the gait models constructed, for which time complexity is high, we consider the area under the spline curve constructed, which is a linear metric, as our gait feature and construct feature vector containing area signals of the sequence of images considered. DCT (Discrete Cosine Transform) is applied to the feature vector to obtain the feature matrix. The dimensional reduction of the constructed feature matrix is achieved by adopting the method of MSPCA (Multi-scale Principal Component Analysis). The classification of the feature vectors is done using K-NN and Neuro-Fuzzy classifiers, for the subjects considered in CASIA datasets A, B and DTW (dynamic time warping) for the subjects in CASIA dataset C.
Highlights
Human identification at a distance has gained a lot of attention recently due to increasing need for video surveillance systems
3) The proposed feature namely, area under the limbs of the subject, is computed after modeling the lower limbs with spline curves, Discrete Cosine Transform (DCT) is applied on the feature matrix created and Multi scale principal component analysis (MSPCA) is adopted for dimensional reduction of area signals extracted
5.2 Experimental Results on CASIA datasets A,B The proposed feature matrix is transformed by applying the Discrete Cosine Transform (DCT) and reduced in dimensionality using the proposed MSPCA method
Summary
Human identification at a distance has gained a lot of attention recently due to increasing need for video surveillance systems. Gait is an attractive feature for human identification at a distance and has gained a lot of interest from computer-vision researchers in the recent past.The genesis of the idea of human tracking can be traced back to Cutting and Kozlowski’s perception experiments based on light point displays[1] [2]. Variations in clothing and footwear, distortions in gait pattern produced by carrying objects or walking speed could make analysis an arduous task. These complexities lead to low recognition rates in the algorithms proposed so far. Existing methods on Gait recognition can be classified into model based ones and holistic ones.
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