Robust gait based human identification on incomplete and multi-view sequences
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Robust gait based human identification on incomplete and multi-view sequences Utkarsh Shreemali1 · Anirban Chakraborty1 Received: 5 May 2020 / Revised: 23 September 2020 / Accepted: 23 October 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Gait based person identification is an important research area in the field of video surveillance. The major challenges faced by gait recognition systems in real-life scenarios include view variance, occlusion and resultant unavailability of a complete sequence containing a gait cycle. In this work, we propose a novel robust gait recognition framework capable of handling these challenges. We show how Gait-Energy-Images (GEIs) can be accurately constructed from largely incomplete input silhouette sequences. This provides an immediate advantage over current literature that assumes availability of complete sequences. We then highlight the shortcoming of most of the current view-invariant models that perform sub-optimal transformation of probe and gallery sequences captured in different views for comparison. We propose a model which jointly estimates and learns the optimal transformation for comparison of probe and gallery GEIs. Through extensive experiments, we show that our proposed framework is able to outperform most state-of-the-art methods on multiple benchmarks. Keywords Video surveillance · Gait based person recognition · Person re-identification · View invariance · Missing data
1 Introduction The task of identifying humans within and across cameras has been a major topic of interest in the video surveillance community. In a general setting of a network of cameras, given a sequence of observations of a target person, the aim is to identify or re-identify that person Utkarsh Shreemali was a graduate student at the Dept. of Computational and Data Sciences, Indian Institute of Science, Bangalore when this research work was carried out as a part of his M.Tech. dissertation. Anirban Chakraborty
[email protected] Utkarsh Shreemali [email protected] 1
Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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across cameras. This task falls under the domain of sequence-to-sequence matching. The sequences observed across cameras tend to have huge variations due to change in viewpoints, presence of occlusions, illumination and pose variations, etc. Gait is defined as a person’s manner of walking. It is an important biometric property since it is difficult to fake and is contact-less, which gives it advantage over other biometric based human recognition methods like iris and finger-print recognition. Thus, gait analysis has great significance in the field of video surveillance for the task of person identification. When a person walks, he/she repeats the walking actions periodically. One such period capturing all the walking actions of a person is called a gait cycle. Gait analysis models work on a sequence of observations containing the entire gait cycle of a person. A
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