Real-time anomaly detection and localization in crowded scenes

Sabokrou, Mohammad; Fathy, Mahmood; Hoseini, Mohammad; Klette, Reinhard

doi:10.1109/cvprw.2015.7301284

Cited by 160 publications

(73 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As can be seen, our method is comparable with the state-of-the-art. NRE is a very simple method based on the sole criteria of reconstruction error and neighborhood relational encoding, while other methods (such as [47] and [49]) are based on intensive spatio-temporal embedding of video content. The experiments show the generality of the proposed approach.…”

Section: Video Anomaly Detectionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Video Anomaly Detectionmentioning

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

View full text Add to dashboard Cite

show abstract

“…According to the rules of the trajectory of normal objects, a new object that does not satisfy the rules is anomalous. Many methods based on trajectory achieve high detection rates in scenes with few people . However, it is difficult to trace objects in situations with too many overlaps.…”

Section: Related Workmentioning

confidence: 99%

“…Many methods based on trajectory achieve high detection rates in scenes with few people. [19][20][21][22] However, it is difficult to trace objects in situations with too many overlaps. Therefore, trajectory-based methods are not suitable for crowded scenes.…”

Section: Related Workmentioning

confidence: 99%

Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance

Liu

Zhang

et al. 2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real‐time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi‐instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real‐time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi‐instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time‐transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time‐series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi‐instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state‐of‐the‐art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.

show abstract

“…The majority of the existing works use optical flow to model the feature representation of videos [1,3,18,31,32]; however, optical flow suffers from the problem of occlusions and camera motion. In this study, we propose principal spatiotemporal-oriented energy to represent the low-level motion feature.…”

Section: Model Mid-level Energy Representations With Cdbnmentioning

confidence: 99%

“…Among the various applications of surveillance videos, anomaly event detection is becoming one of the fundamental challenges and has attracted considerable attention from both academia and industry in recent years [1][2][3][4]. However, it is still relatively difficult to design a general framework for anomaly event detection and localization owing to the typical difficulties of anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Huang

Dong-jun

Zhou

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

show abstract

Real-time anomaly detection and localization in crowded scenes

Cited by 160 publications

References 23 publications

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Self-Supervised Representation Learning via Neighborhood-Relational Encoding

Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance

Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Contact Info

Product

Resources

About