Toward Abnormal Trajectory and Event Detection in Video Surveillance

Coşar, Serhan; Donatiello, Giuseppe; Bogorny, Vânia; Gárate, Carolina; Älvares, Luís Otávio; Brémond, François

doi:10.1109/tcsvt.2016.2589859

Cited by 164 publications

(79 citation statements)

References 25 publications

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“…Deep Learning has elevated the performance of captioning models with images and videos. Most of related work [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] follow a multimodal framework which combines CNN [26], [27] and RNN like Long Short-Term Memory (LSTM) [28] and Gated Recurrent Unit (GRU) [29]. Visual features in highlevel with semantic information are first extracted by the CNN encoder, while the RNN decoder predicts the description word by word according to visual features.…”

Section: Related Workmentioning

confidence: 99%

Cascaded Revision Network for Novel Object Captioning

Feng

Fan

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Image captioning, a challenging task where the machine automatically describes an image by sentences, has drawn significant attention in recent years. Despite the remarkable improvements of recent approaches, however, these methods are built upon a large set of training image-sentence pairs. The expensive labor efforts hence limit the captioning model to describe the wider world. In this paper, we present a novel network structure, Cascaded Revision Network, which aims at relieving the problem by equipping the model with outof-domain knowledge. CRN first tries its best to describe an image using the existing vocabulary from in-domain knowledge. Due to the lack of out-of-domain knowledge, the caption may be inaccurate or include ambiguous words for the image with unknown (novel) objects. We propose to re-edit the primary captioning sentence by a series of cascaded operations. We introduce a perplexity predictor to find out which words are most likely to be inaccurate given the input image. Thereafter, we utilize external knowledge from a pre-trained object detection model and select more accurate words from detection results by the visual matching module. In the last step, we design a semantic matching module to ensure that the novel object is fit in the right position. By this novel cascaded captioning-revising mechanism, CRN can accurately describe images with unseen objects. We validate the proposed method with state-of-the-art performance on the held-out MSCOCO dataset as well as scale to ImageNet, demonstrating the effectiveness of this method.

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Section: Related Workmentioning

confidence: 99%

Cascaded Revision Network for Novel Object Captioning

Feng

Fan

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…In addition, trajectory-wise descriptors mainly detect objects with unusual routes, while the body action of objects, such as jumping or falling down, cannot be detected. Coşar et al [13] considered the pros and cons of trajectory-wise descriptors and proposed a unified AED framework by incorporating both trajectory-and pixel-wise analysis.…”

Section: ) Trajectory-wise Descriptormentioning

confidence: 99%

Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor

Duan

et al. 2018

EURASIP J. Adv. Signal Process.

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Detecting abnormal events in crowded scenes is an important but challenging task in computer vision. Contextual information is useful for discovering salient events in scenes; however, it cannot be characterized well by commonly used pixel-based descriptors, such as the HOG descriptor. In this paper, we propose contextual gradients between two local regions and then construct a histogram of oriented contextual gradient (HOCG) descriptor for abnormal event detection based on the contextual gradients. The HOCG descriptor is a distribution of contextual gradients of sub-regions in different directions, which can effectively characterize the compositional context of events. We conduct extensive experiments on several public datasets and compare the experimental results using state-of-the-art approaches. Qualitative and quantitative analysis of experimental results demonstrate the effectiveness of the proposed HOCG descriptor.

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“…A study in such direction was carried out by Cosar et al [25] where both problems of analysis of behavior as well as abnormal detection of behavior are jointly addressed using trajectory and pixelbased information. The technique also implements clustering mechanism on the analyzed grid to perform detection.…”

Section: Related Techniquesmentioning

confidence: 99%

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

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Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

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Toward Abnormal Trajectory and Event Detection in Video Surveillance

Cited by 164 publications

References 25 publications

Cascaded Revision Network for Novel Object Captioning

Cascaded Revision Network for Novel Object Captioning

Abnormal event detection in crowded scenes using histogram of oriented contextual gradient descriptor

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

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