72,264 research outputs found
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
Skeleton-based Action Recognition of People Handling Objects
In visual surveillance systems, it is necessary to recognize the behavior of
people handling objects such as a phone, a cup, or a plastic bag. In this
paper, to address this problem, we propose a new framework for recognizing
object-related human actions by graph convolutional networks using human and
object poses. In this framework, we construct skeletal graphs of reliable human
poses by selectively sampling the informative frames in a video, which include
human joints with high confidence scores obtained in pose estimation. The
skeletal graphs generated from the sampled frames represent human poses related
to the object position in both the spatial and temporal domains, and these
graphs are used as inputs to the graph convolutional networks. Through
experiments over an open benchmark and our own data sets, we verify the
validity of our framework in that our method outperforms the state-of-the-art
method for skeleton-based action recognition.Comment: Accepted in WACV 201
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Object detection in wide area motion imagery (WAMI) has drawn the attention
of the computer vision research community for a number of years. WAMI proposes
a number of unique challenges including extremely small object sizes, both
sparse and densely-packed objects, and extremely large search spaces (large
video frames). Nearly all state-of-the-art methods in WAMI object detection
report that appearance-based classifiers fail in this challenging data and
instead rely almost entirely on motion information in the form of background
subtraction or frame-differencing. In this work, we experimentally verify the
failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a
heatmap-based fully convolutional neural network (CNN), and propose a novel
two-stage spatio-temporal CNN which effectively and efficiently combines both
appearance and motion information to significantly surpass the state-of-the-art
in WAMI object detection. To reduce the large search space, the first stage
(ClusterNet) takes in a set of extremely large video frames, combines the
motion and appearance information within the convolutional architecture, and
proposes regions of objects of interest (ROOBI). These ROOBI can contain from
one to clusters of several hundred objects due to the large video frame size
and varying object density in WAMI. The second stage (FoveaNet) then estimates
the centroid location of all objects in that given ROOBI simultaneously via
heatmap estimation. The proposed method exceeds state-of-the-art results on the
WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped
objects, as well as being the first proposed method in wide area motion imagery
to detect completely stationary objects.Comment: Main paper is 8 pages. Supplemental section contains a walk-through
of our method (using a qualitative example) and qualitative results for WPAFB
2009 datase
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