Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

Sharif, Md. Haidar; Jiao, Lei; Omlin, Christian W.

doi:10.3390/electronics12071517

Cited by 7 publications

(1 citation statement)

References 129 publications

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“…In predictive techniques, the probability of the desired outcome is predicted in line with the significance in the dataset of input variables utilizing known outcomes while training the model. Several other techniques such as fuzzy theory estimation [58], adaptive sparsity model [59], sparsity-based background subtraction method [60], use of high-frequency correlation sensors [61], particle filtering [62], redundancy removal [63] are utilized in the literature to spot irregularities in the flow of traffic such as accidents, dangerous driving behavior, street crimes, traffic violations, etc.…”

Section: ) Modeling-basedmentioning

confidence: 99%

Enhancement of Video Anomaly Detection Performance Using Transfer Learning and Fine-Tuning

Dilek,

Dener

2024

IEEE Access

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The use of surveillance cameras is a common solution for the need to manage the urban traffic that arises with the effect of the increment in the population in the cities and to provide security. As the number of surveillance cameras rises, video streams that create big data are recorded. Analysis of video streams collected from those traffic surveillance cameras, automatic detection of unusual, suspicious events, as well as a range of harmful activities, become crucial because it is impossible to observe, analyze, and comprehend the contents of these movies using human labor. Recent studies have shown that deep learning (DL)-based artificial intelligence (AI) techniques, particularly machine learning (ML) techniques, are used in video anomaly detection (VAD) studies. In this study, an efficient frame-level VAD method is proposed based on transfer learning (TL) and fine-tuning (FT) approach and anomalies were detected using 20 popular convolutional neural networks (CNN) based DL models where variants of VGG, Xception, MobileNet, Inception, EfficientNet, ResNet, DenseNet, NASNet and ConvNeXt base models were trained using TL and FT approach. The proposed approach was tested using CUHK Avenue, UCSD Ped1 and UCSD Ped2 datasets and the performance of the models were measured using Area Under Curve (AUC), accuracy, precision, recall, and F1-score metrics. The highest AUC scores measured were 100%, 100% and 98.41% for the UCSD Ped1, UCSD Ped2 and CUHK Avenue datasets respectively. Compared to existing techniques in the literature, experimental results show that the suggested method offers state-of-the-art (SOTA) VAD performance.

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Section: ) Modeling-basedmentioning

confidence: 99%

Enhancement of Video Anomaly Detection Performance Using Transfer Learning and Fine-Tuning

Dilek,

Dener

2024

IEEE Access

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Spatial Attention Transformer Based Framework for Anomaly Classification in Image Sequences

Garg,

Nigam,

Singh

et al. 2024

Lecture Notes in Computer Science

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Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis

Merlin,

Karthick,

Babu

et al. 2024

Sci Rep

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In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have been used in the field still many problems remain to get the abnormal events detection accuracy. Moreover, many feature representations have limited capability to describe the content since several research works applied hand craft features, this type of feature can work in limited problems. To overcome this problem, this paper introduced the novel feature descriptor namely STS-D (Spatial and Temporal Saliency - Descriptor), which includes spatial and temporal information of the objects. This feature descriptor efficiently describes the shape and speed of the object. To find the anomaly score, fuzzy representation is modeled to efficiently differentiate the normal and abnormal events using fuzzy membership degree. The benchmark datasets UMN, UCSD Ped1 and Ped2 and real time roadway surveillance dataset are used to evaluate the performance of the proposed approach. Also, several existing abnormal events detection approaches are used to compare with the proposed method to evaluate the effectiveness of the proposed work.

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Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks

Cited by 7 publications

References 129 publications

Enhancement of Video Anomaly Detection Performance Using Transfer Learning and Fine-Tuning

Enhancement of Video Anomaly Detection Performance Using Transfer Learning and Fine-Tuning

Spatial Attention Transformer Based Framework for Anomaly Classification in Image Sequences

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis

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