Distinguishing Pedestrians Facing to the Front and the Side by Gait Observation

In this paper, we propose a method to distinguish pedestrians facing to the front and the side by using a low resolution and quality surveillance image sequence. In the past, there have been many methods to estimate the head orientation of a pedestrian. However, because all these methods use facial texture information to achieve the goal, it is difficult to apply the methods to a low resolution and quality image sequence that does not include enough information. Therefore, we focus on the gait change of a pedestrian, which can be acquired even from a low-resolution silhouette sequence. Experiments confirm the effectiveness of the proposed method by using low-resolution image sequences of over one hundred subjects.

[1]  Jean-Marc Odobez,et al.  Tracking the Visual Focus of Attention for a Varying Number of Wandering People , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Teera Siriteerakul Advance in Head Pose Estimation from Low Resolution Images: A Review , 2012 .

[3]  Yuxiao Hu,et al.  Head Pose Estimation in Seminar Room Using Multi View Face Detectors , 2006, CLEAR.

[4]  Yasushi Makihara,et al.  The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition , 2012, IEEE Transactions on Information Forensics and Security.

[5]  Jamal Ahmad Dargham,et al.  Gait Recognition using Gait Energy Image , 2011 .

[6]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.

[7]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Vittorio Murino,et al.  Multi-class Classification on Riemannian Manifolds for Video Surveillance , 2010, ECCV.

[9]  Yasushi Makihara,et al.  Gait Recognition Using a View Transformation Model in the Frequency Domain , 2006, ECCV.

[10]  Ian D. Reid,et al.  Guiding Visual Surveillance by Tracking Human Attention , 2009, BMVC.

[11]  Mark S. Nixon,et al.  On using gait biometrics to enhance face pose estimation , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[12]  Yasushi Makihara,et al.  Gait-based age estimation using a whole-generation gait database , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[13]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.