1,720,966 research outputs found
Social Groups Detection in Crowd through Shape-Augmented Structured LearningImage Analysis and Processing – ICIAP 2013
No full textMost of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups, by injecting in the model socially plausible shape configurations. By taking into account social groups patterns, the system is able to outperform state of the art methods on two publicly available benchmark sets of videos
Socially Constrained Structural Learning for Groups Detection in Crowd
Full text linkModern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of
individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering
procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory,
Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal
with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on
both ground truth trajectories and tracklets previously extracted by available detector/tracker systems
Tracking social groups within and across cameras
Full text linkWe propose a method for tracking groups from single and multiple cameras with disjoint fields of view. Our formulation follows the tracking-by-detection paradigm where groups are the atomic entities and are linked over time to form long and consistent trajectories. To this end, we formulate the problem as a supervised clustering problem where a Structural SVM classifier learns a similarity measure appropriate for group entities. Multi-camera group tracking is handled inside the framework by adopting an orthogonal feature encoding that allows the classifier to learn inter- and intra-camera feature weights differently. Experiments were carried out on a novel annotated group tracking data set, the DukeMTMC-Groups data set. Since this is the first data set on the problem it comes with the proposal of a suitable evaluation measure. Results of adopting learning for the task are encouraging, scoring a +15% improvement in F1 measure over a non-learning based clustering baseline. To our knowledge this is the first proposal of this kind dealing with multi-camera group tracking
Learning to identify leaders in crowd
No full textLeader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde's work on crowd psychology, individuals are modeled as nodes of a directed graph and leaders inherits their relevance thanks to other members references. We note this is analogous to the way websites are ranked by the PageRank algorithm. During experiments, we observed different feature weights depending on the specific type of crowd, highlighting the impossibility to provide a unique interpretation of leadership. To our knowledge, this is the first attempt to study leader identification as a metric learning proble
Structured learning for detection of social groups in crowd
No full textGroup detection in crowds will play a key role in future behavior analysis surveillance systems. In this work we build a new Structural SVM-based learning framework able to solve the group detection task by exploiting annotated video data to deduce a sociologically motivated distance measure founded on Hall's proxemics and Granger's causality. We improve over state-of-the-art results even in the most crowded test scenarios, while keeping the classification time affordable for quasi-real time applications. A new scoring scheme specifically designed for the group detection task is also proposed
Learning to Divide and Conquer for Online Multi-Target Tracking
No full textOnline Multiple Target Tracking (MTT) is often addressed within the
tracking-by-detection paradigm. Detections are previously extracted
independently in each frame and then objects trajectories are built by
maximizing specifically designed coherence functions. Nevertheless, ambiguities
arise in presence of occlusions or detection errors. In this paper we claim
that the ambiguities in tracking could be solved by a selective use of the
features, by working with more reliable features if possible and exploiting a
deeper representation of the target only if necessary. To this end, we propose
an online divide and conquer tracker for static camera scenes, which partitions
the assignment problem in local subproblems and solves them by selectively
choosing and combining the best features. The complete framework is cast as a
structural learning task that unifies these phases and learns tracker
parameters from examples. Experiments on two different datasets highlights a
significant improvement of tracking performances (MOTA +10%) over the state of
the art
Learning Where to Attend Like a Human Driver
No full textDespite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task.
In this paper we study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at?
Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset. Experimental comparison against different baselines reveal that the driver's gaze can indeed be learnt to some extent, despite i) being highly subjective and ii) having only one driver's gaze available for each sequence due to the irreproducibility of the scene. Eventually, we advocate for a new assisted driving paradigm which suggests to the driver, with no intervention, where she should focus her attention
DR(eye)VE: a Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving
No full textAutonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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