1,305 research outputs found
Veliki nadzorni sustav: detekcija i praćenje sumnjivih obrazaca pokreta u prometnim gužvama
The worldwide increasing sentiment of insecurity gave birth to a new era, shaking thereby the intelligent video-surveillance systems design and deployment. The large-scale use of these means has prompted the creation of new needs in terms of analysis and interpretation. For this purpose, behavior recognition and scene understanding related applications have become more captivating to a significant number of computer vision researchers, particularly when crowded scenes are concerned. So far, motion analysis and tracking remain challenging due to significant visual ambiguities, which encourage looking into further keys. By this work, we present a new framework to recognize various motion patterns, extract abnormal behaviors and track them over a multi-camera traffic surveillance system. We apply a density-based technique to cluster motion vectors produced by optical flow, and compare them with motion pattern models defined earlier. Non-identified clusters are treated as suspicious and simultaneously tracked over an overlapping camera network for as long as possible. To aiming the network configuration, we designed an active camera scheduling strategy where camera assignment was realized via an improved Weighted Round-Robin algorithm. To validate our approach, experiment results are presented and discussed.Širom svijeta rasprostranjeni osjećaj nesigurnosti postavio je temelje za dizajniranje i implementaciju inteligentnih sustava nadzora. Velika upotreba ovih sredstava potaknula je stvaranje novih potreba analize i interpretacije. U ovu svrhu, prepoznavanje ponašanja i razumijevanje prizora postaju sve privlačnije povezane primjene značajnom broju istraživača računalne vizije, posebno kada se radi o vrlo prometnim prizorima. Analiza pokreta i slijeđenja ostalo je izazovno područje zbog značajnih vizualnih nejasnoća koje zahtijevaju daljnja istraživanja. U radu je prikazan novi okvir za prepoznavanje različitih uzoraka pokreta, izoliranje neprirodnih ponašanja i njihovo praćenje pomoću nadzornog sustava prometa s više kamera. Primjenjuje se na gustoći zasnovana tehnika skupa vektora pokreta sastavljenih iz optičkog toka te uspoređenih s ranije definiranim modelima uzoraka. Neidentificirani skupovi tretiraju se kao sumnjivi i istovremeno su praćeni mrežom s više preklapajućih kamera što je duže moguće. S ciljem konfiguriranja mreže, dizajnirana je strategija raspoređivanja aktivnih kamera gdje je dodjela kamere ostvarena pomoću unaprijeđenog "Weighted Round-Robin" algoritma
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to
classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD
descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising
advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
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