550 research outputs found
Pyramidal Fisher Motion for Multiview Gait Recognition
The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous
works) we propose and evaluate the use of motion descriptors based on densely
sampled short-term trajectories. We take advantage of state-of-the-art people
detectors to define custom spatial configurations of the descriptors around the
target person. Thus, obtaining a pyramidal representation of the gait motion.
The local motion features (described by the Divergence-Curl-Shear descriptor)
extracted on the different spatial areas of the person are combined into a
single high-level gait descriptor by using the Fisher Vector encoding. The
proposed approach, coined Pyramidal Fisher Motion, is experimentally validated
on the recent `AVA Multiview Gait' dataset. The results show that this new
approach achieves promising results in the problem of gait recognition.Comment: Submitted to International Conference on Pattern Recognition, ICPR,
201
Automatic learning of gait signatures for people identification
This work targets people identification in video based on the way they walk
(i.e. gait). While classical methods typically derive gait signatures from
sequences of binary silhouettes, in this work we explore the use of
convolutional neural networks (CNN) for learning high-level descriptors from
low-level motion features (i.e. optical flow components). We carry out a
thorough experimental evaluation of the proposed CNN architecture on the
challenging TUM-GAID dataset. The experimental results indicate that using
spatio-temporal cuboids of optical flow as input data for CNN allows to obtain
state-of-the-art results on the gait task with an image resolution eight times
lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN)
for gait recognition. Data and code:
http://www.uco.es/~in1majim/research/cnngaitof.htm
Fisher Motion Descriptor for Multiview Gait Recognition
The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes
as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based
on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to de ne
custom spatial con gurations of the descriptors around the target person, obtaining a rich representation of
the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [1]) extracted
on the di erent spatial areas of the person are combined into a single high-level gait descriptor by using
the Fisher Vector encoding [2]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally
validated on `CASIA' dataset [3] (parts B and C), `TUM GAID' dataset [4], `CMU MoBo' dataset [5] and the
recent `AVA Multiview Gait' dataset [6]. The results show that this new approach achieves state-of-the-art
results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on
single and multiple camera setups, wearing di erent clothes, carrying bags, walking at diverse speeds and
not limited to straight walking paths
Pyramidal Fisher Motion for Multiview Gait Recognition
Submitted to International Conference on Pattern Recognition, ICPR, 2014The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database
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