Abstract
Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.
Highlights
Gait is the periodic motion pattern of human walking or running
The data from a subject is simultaneously captured by 11 USB cameras around the left hand side of the subject when he/she was walking, and the angle between two nearest view directions is 18◦
When a subject walked in the scene, he/she was asked to walk naturally along a straight line 6 times first, and 11 × 6 = 66 normal walking video sequences were captured for each subject
Summary
Different people owns different gait patterns, due to the reason that gait pattern is uniquely decided by the personal factors, such as personal habits, injury, and disease. Base on this character, researchers in pattern recognition area employ gait pattern to recognition the identity of walkers, namely gait recognition. Gait pattern is used for disease diagnosing by the researchers in the field of medicine, namely gait analysis. No matter gait recognition or gait analysis, gait events detection is the basic problem of the both applications. Automatic detection of gait events is desirable for artificial intelligence applications, such as gait recognition and medicine abnormal gait analysis [1]
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