We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.

In this paper we propose a generic framework based on Hidden Markov Models (HMMs) for recognition of individuals from their gait. The HMM framework is suitahle, because the gait of an individual can be visualized as hk adopting postures from a set, io a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can he regarded as the states of the HMM and are typical to that individual and provide a m m s of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the paflicular feature vector used to represent the gait information contained in the posNres. The framework, thus, provides flexibility in the selection of the feature vector, The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the LI and Lz norms of the vector difference, and the normalized inner product of the vectos, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before.

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The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. At the end of three years, the system we developed outperformed all 9 other teams in final blind tests over previously-unseen terrain. In this paper we describe the system, as well as the two learning techniques that led to this success: online path learning and map reuse.