14 research outputs found
Recurrent Attention Models for Depth-Based Person Identification
We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning task, our model is based on a combination of
convolutional and recurrent neural networks with the goal of identifying small,
discriminative regions indicative of human identity. We demonstrate that our
model produces state-of-the-art results on several published datasets given
only depth images. We further study the robustness of our model towards
viewpoint, appearance, and volumetric changes. Finally, we share insights
gleaned from interpretable 2D, 3D, and 4D visualizations of our model's
spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201
Ensemble of Different Approaches for a Reliable Person Re-identification System
An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/
A multi-viewpoint feature-based re-identification system driven by skeleton keypoints
Thanks to the increasing popularity of 3D sensors, robotic vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benefits, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to different representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identification system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identification. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced, which is capable of dealing with many people in the scene, coping with the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and can be applied to any kind of body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both body pose estimation and re-identification. The proposed approach was also compared with a skeletal tracking system working on 3D data: the comparison assessed the good performance level of the multi-viewpoint approach. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint
Autonomous Person-Specific Following Robot
Following a specific user is a desired or even required capability for
service robots in many human-robot collaborative applications. However, most
existing person-following robots follow people without knowledge of who it is
following. In this paper, we proposed an identity-specific person tracker,
capable of tracking and identifying nearby people, to enable person-specific
following. Our proposed method uses a Sequential Nearest Neighbour with
Thresholding Selection algorithm we devised to fuse together an anonymous
person tracker and a face recogniser. Experiment results comparing our proposed
method with alternative approaches showed that our method achieves better
performance in tracking and identifying people, as well as improved robot
performance in following a target individual
A neural network approach to human posture classification and fall detection using RGB-D camera
In this paper, we describe a human posture classification and a falling detector module suitable for smart homes and assisted living solutions. The system uses a neural network that processes the human joints produced by a skeleton tracker using the depth streams of an RGB-D sensor. The neural network is able to recognize standing, sitting and lying postures. Using only the depth maps from the sensor, the system can work in poor light conditions and guarantees the privacy of the person. The neural network is trained with a dataset produced with the Kinect tracker, but it is also tested with a different human tracker (NiTE). In particular, the aim of this work is to analyse the behaviour of the neural network even when the position of the extracted joints is not reliable and the provided skeleton is confused. Real-time tests have been carried out covering the whole operative range of the sensor (up to 3.5 m). Experimental results have shown an overall accuracy of 98.3% using the NiTE tracker for the falling tests, with the worst accuracy of 97.5%
Learning nuisances to track pedestrians in autonomous vehicles
Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem as a data association one where they proposed various representations aiming for invariance to nuisances such as viewpoint changes, body deformation, object occlusion, and illumination changes. However, these methods still suffer to address abrupt changes since they do not explicitly model the nature of the nuisances. In this work, we propose to train a classifier that recognizes these nuisances, more specifically rotational body deformation of pedestrians. We aim to detect deformations as a method to find a good representation that will lead to better tracking of pedestrians as well as other tasks
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
Gait-based person re-identification (Re-ID) is valuable for safety-critical
applications, and using only 3D skeleton data to extract discriminative gait
features for person Re-ID is an emerging open topic. Existing methods either
adopt hand-crafted features or learn gait features by traditional supervised
learning paradigms. Unlike previous methods, we for the first time propose a
generic gait encoding approach that can utilize unlabeled skeleton data to
learn gait representations in a self-supervised manner. Specifically, we first
propose to introduce self-supervision by learning to reconstruct input skeleton
sequences in reverse order, which facilitates learning richer high-level
semantics and better gait representations. Second, inspired by the fact that
motion's continuity endows temporally adjacent skeletons with higher
correlations ("locality"), we propose a locality-aware attention mechanism that
encourages learning larger attention weights for temporally adjacent skeletons
when reconstructing current skeleton, so as to learn locality when encoding
gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are
built using context vectors learned by locality-aware attention, as final gait
representations. AGEs are directly utilized to realize effective person Re-ID.
Our approach typically improves existing skeleton-based methods by 10-20%
Rank-1 accuracy, and it achieves comparable or even superior performance to
multi-modal methods with extra RGB or depth information. Our codes are
available at https://github.com/Kali-Hac/SGE-LA.Comment: Accepted at IJCAI 2020 Main Track. Sole copyright holder is IJCAI.
Codes are available at https://github.com/Kali-Hac/SGE-L
People Identification Based on Person Image and Additional Physical Parameters Comparison
This paper proposes and presents one approach for people identification based on image and additional physical parameters, height and step length, of a person comparison. People identification is very important in many areas of human life. There are large number of identification methods (biometric methods) that include a different scope of methods, for example fingerprint identification, hand geometry identification, facial recognition, methods based on human eye identification (retina and iris), gait recognition etc. Most of that methods require some kind of interaction with a person, what could be a problem in many practical applications. The method that does not require any interaction with a person is gait recognition. One approach for a people identification based on gait recognition, that uses silhouettes of a person and parameters of person height and step length, is proposed and presented in this paper