Fast and accurate detection and localization of abnormal behavior in crowded scenes

Sabokrou, Mohammad; Fathy, Mahmood; Moayed, Zahra; Klette, Reinhard

doi:10.1007/s00138-017-0869-8

Cited by 24 publications

(28 citation statements)

References 56 publications

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“…deep networks has often been defined by minimizing the reconstruction error [34], such as in auto-encoders (AEs). AEs have shown to be great tools for unsupervised representation learning in a variety of tasks, including image inpainting [43], feature ranking [54], denosing [57], clustering [65], defense against adversarial examples [35], and anomaly detection [48,52]. Although AEs have led to farreaching success for data representation, there are some caveats associated with using reconstruction errors as the sole metric for representation learning: (1) As also argued in [58], it forces to reconstruct all parts of the input, even if they are irrelevant for any given task or are contaminated by noise; (2) It leads to a mechanism that entirely depends on single-point data abstraction, i.e., the AE learns to just reconstruct its input while neglecting other data points present in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Sabokrou

Khalooei

Adeli

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

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In this paper, we propose a novel self-supervised representation learning by taking advantage of a neighborhoodrelational encoding (NRE) among the training data. Conventional unsupervised learning methods only focused on training deep networks to understand the primitive characteristics of the visual data, mainly to be able to reconstruct the data from a latent space. They often neglected the relation among the samples, which can serve as an important metric for self-supervision. Different from the previous work, NRE aims at preserving the local neighborhood structure on the data manifold. Therefore, it is less sensitive to outliers. We integrate our NRE component with an encoder-decoder structure for learning to represent samples considering their local neighborhood information. Such discriminative and unsupervised representation learning scheme is adaptable to different computer vision tasks due to its independence from intense annotation requirements. We evaluate our proposed method for different tasks, including classification, detection, and segmentation based on the learned latent representations. In addition, we adopt the auto-encoding capability of our proposed method for applications like defense against adversarial example attacks and video anomaly detection. Results confirm the performance of our method is better or at least comparable with the state-of-the-art for each specific application, but with a generic and self-supervised approach.

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Section: Introductionmentioning

confidence: 99%

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Sabokrou

Khalooei

Adeli

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

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“…For Ped 1, we compare our proposed approach both to traditional methods (SRC [6], MPPCA [43], and MDT [40]) and high-level deep learning-based methods (AVID [19], Sabokrou [8], and deep cascade [16]). As introduced and calculated in [26], evaluation metrics such as equal error rate (EER) and area under curve (AUC) are computed at frame level and compared to the state-of-the-art methods.…”

Section: Resultsmentioning

confidence: 99%

“…Reconstruction was done as a linear combination of dictionary bases which are representative of all normal samples. Dictionary can be learned offline through codebook generation or online through updating along with observing new normal samples [8].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

Rezaei

Yazdi

2021

Wireless Communications and Mobile Computing

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Recently, attention toward autonomous surveillance has been intensified and anomaly detection in crowded scenes is one of those significant surveillance tasks. Traditional approaches include the extraction of handcrafted features that need the subsequent task of model learning. They are mostly used to extract low-level spatiotemporal features of videos, neglecting the effect of semantic information. Recently, deep learning (DL) methods have been emerged in various domains, especially CNN for visual problems, with the ability to extract high-level information at higher layers of their architectures. On the other side, topic modeling-based approaches like NMF can extract more semantic representations. Here, we investigate a new hybrid visual embedding method based on deep features and a topic model for anomaly detection. Features per frame are computed hierarchically through a pretrained deep model, and in parallel, topic distributions are learned through multilayer nonnegative matrix factorization entangling information from extracted deep features. Training is accomplished through normal samples. Thereafter, K -means is applied to find typical normal clusters. At test time, after achieving feature representation through deep model and topic distribution for test frames, a statistical earth mover distance (EMD) metric is evaluated to measure the difference between normal cluster centroids and test topic distributions. High difference versus a threshold is detected as an anomaly. Experimental results on the benchmark Ped1 and Ped2 UCSD datasets demonstrate the effectiveness of our proposed method in anomaly detection.

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“…In both cases, a Gaussian model was constructed to represent the normal pattern, and new patches were marked as anomalous if both classifiers gave an anomalous response. Same authors refined their work in [60] , using the local descriptor as fast rejector of easy patches. When a new patch arrived, the local descriptor classified it.…”

Section: Deep Learning For Crowd Anomaly Detection: Approaches and Numentioning

confidence: 99%

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Sánchez

Hupont²,

Tabik

et al. 2020

Information Fusion

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Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed.

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Fast and accurate detection and localization of abnormal behavior in crowded scenes

Cited by 24 publications

References 56 publications

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

A New Semantic and Statistical Distance‐Based Anomaly Detection in Crowd Video Surveillance

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

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