4,170 research outputs found
Robust pedestrian detection and tracking in crowded scenes
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases
Human robot interaction in a crowded environment
Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3].
Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from peopleās movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robotās interaction, it may approach the person.
Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]
Tracking people in crowds by a part matching approach
The major difficulty in human tracking is the problem raised by challenging occlusions where the target person is repeatedly and extensively occluded by either the background or another moving object. These types of occlusions may cause significant changes in the person's shape, appearance or motion, thus making the data association problem extremely difficult to solve. Unlike most of the existing methods for human tracking that handle occlusions by data association of the complete human body, in this paper we propose a method that tracks people under challenging spatial occlusions based on body part tracking. The human model we propose consists of five body parts with six degrees of freedom and each part is represented by a rich set of features. The tracking is solved using a layered data association approach, direct comparison between features (feature layer) and subsequently matching between parts of the same bodies (part layer) lead to a final decision for the global match (global layer). Experimental results have confirmed the effectiveness of the proposed method. Ā© 2008 IEEE
Contextual anomaly detection in crowded surveillance scenes
AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
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