Novel dataset for fine-grained abnormal behavior understanding in crowd

Rabiee, Hamidreza; Haddadnia, Javad; Mousavi, Hossein; Kalantarzadeh, Maziyar; Nabi, Moin; Murino, Vittorio

doi:10.1109/avss.2016.7738074

Cited by 52 publications

(16 citation statements)

References 19 publications

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“…The UMN dataset is employed most often, twice in this review. This Unusual Crowd Activity Dataset of the University of Minnesota [51] [53]), Violent Flows (uncontrolled videos including violence [54]), and UCF (videos with normal and abnormal urban crowd scenes [55]). Lastly, the Video-Level Group Affect (VGAF) database is proposed in [25].…”

Section: Video Datasetsmentioning

confidence: 99%

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

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This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

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Section: Video Datasetsmentioning

confidence: 99%

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

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“…Most anomaly detection methods are based on motion information which use hand-crafted features to model normal-activity patterns [7][8][9][10][11][12][13][14][15][16]. On the other hand our method uses the entire set of motion vectors obtained from the video as in [17], [18], [19].…”

Section: Motion Vector Based Anomaly Detectionmentioning

confidence: 99%

Robust and Computationally-Efficient Anomaly Detection Using Powers-Of-Two Networks

Muneeb

Koyuncu

Keshtkarjahromi

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. We reduce the complexity of the network by denoising the intermediate layer outputs of the CNN and by using powers-of-two weights, which replaces the computationally expensive multiplication operations with bit-shift operations. Denoising operation during inference forces small valued intermediate layer outputs to zero. The number of zeros in the network significantly increases as a result of denoising, we can implement the CNN about 10% faster than a comparable network while detecting all the anomalies in the testing set. It turns out that denoising operation also provides robustness because the contribution of small intermediate values to the final result is negligible. During training we also generate motion vector images by a Generative Adversarial Network (GAN) to improve the robustness of the overall system. We experimentally observe that the resulting system is robust to background motion.

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“…Focusing on groups and crowds, the authors of [30] present the Atomic Group Action dataset targeting the dynamics of group formation, yet no meaningful emotional behavior is exhibit. Rabiee's dataset [31] provides some emotional-rich situations such as panic and fight, although in a staged way. Finally, the S-hock dataset [32] focuses on the behavior of spectator crowds with rich annotations at the individual level, enabling the addition of further affective annotations although restricted to this type of crowds.…”

Section: Crowd Analysis Datasetsmentioning

confidence: 99%

Emotion estimation in crowds: a survey

Urizar

Barakova

Regazzoni

et al. 2017

8th International Conference of Pattern Recognition Systems (ICPRS 2017)

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Emotions play an important role in human behavior, even more so in large congregations of people where emotional states are prompt to be contaged and amplified. This work presents a qualitative systematic review of the literature concerning the estimation of emotions and affects in real-life crowded environments, covering the aspects of methods and datasets. The academic search engine Scopus was inquired and the search was limited to publications in the English language addressing any of the aforementioned aspects. The aim of this contribution is to highlight advances, limitation and trends in addressing the estimation of emotions in crowds.

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Novel dataset for fine-grained abnormal behavior understanding in crowd

Cited by 52 publications

References 19 publications

Automatic Emotion Recognition for Groups: A Review

Automatic Emotion Recognition for Groups: A Review

Robust and Computationally-Efficient Anomaly Detection Using Powers-Of-Two Networks

Emotion estimation in crowds: a survey

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