Deep Appearance Features for Abnormal Behavior Detection in Video

Smeureanu, Sorina; Ionescu, Radu Tudor; Popescu, Marius; Alexe, Bogdan

doi:10.1007/978-3-319-68548-9_70

Cited by 95 publications

(71 citation statements)

References 25 publications

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“…We evaluate our approach in comparison with a series of state-of-the-art methods [6,7,9,11,12,13,15,21,22,23,24,25,26,27,28,31,32,33,36,37,38] on the Avenue, the ShanghaiTech, the UCSD Ped2 and the UMN data sets. The corresponding results are presented in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Several abnormal event detection approaches [5,6,9,23,29] learn a dictionary of atoms representing normal events during training, then label the events not represented in the dictionary as abnormal. Some recent approaches have employed locality sensitive hashing [38] and deep learning [11,12,21,24,27,28,31,33,36,37] to achieve better results. For instance, Smeureanu et al [33] employed a one-class Support Vector Machines (SVM) model based on deep features provided by convolutional neural networks (CNN) pre-trained on the ILSVRC benchmark [30], while Ravanbakhsh et al [27] combined pre-trained CNN models with low-level optical-flow maps.…”

Section: Related Workmentioning

confidence: 99%

“…Abnormal event detection in video has drawn a lot of attention in the past couple of years [7,11,12,13,14,21,22,24,27,28,31,33,34,36,37,38], perhaps because it is considered a challenging task due to the commonly accepted definition of abnormal events, which relies on context. An example that illustrates the importance of context is a scenario in which a truck is being driven on the street (normal event) versus a scenario in which a truck is being driven in a pedestrian area (abnormal event).…”

Section: Introductionmentioning

confidence: 99%

“…In general, existing abnormal event detection frameworks extract features at a local level [7,9,15,22,23,24,25,31,32,38], global (frame) level [21,26,27,28,33], or both [5,6,11]. All these approaches extract features without explicitly taking into account the objects of interest.…”

Section: Introductionmentioning

confidence: 99%

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Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video

Ionescu

Khan

Georgescu

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

292

166

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Abnormal event detection in video is a challenging vision problem. Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regarding abnormal events, these methods are not fully-equipped to differentiate between normal and abnormal events. In this work, we formalize abnormal event detection as a one-versus-rest binary classification problem. Our contribution is two-fold. First, we introduce an unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information. Second, we propose a supervised classification approach based on clustering the training samples into normality clusters. A one-versus-rest abnormal event classifier is then employed to separate each normality cluster from the rest. For the purpose of training the classifier, the other clusters act as dummy anomalies. During inference, an object is labeled as abnormal if the highest classification score assigned by the one-versus-rest classifiers is negative. Comprehensive experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and UMN. Our approach provides superior results on all four data sets. On the large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4% in terms of frame-level AUC compared to the state-of-the-art method [34].

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video

Ionescu

Khan

Georgescu

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

292

166

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show abstract

“…Typical structures of image reconstruction and translation are usually employed and the difference between their output and ground truth is used to indicate the frame-level score [11,37,25]. Some researchers apply pretrained classification models (such as VGG [41]) to extract useful features from input videos [42,16]. Results of object detection and/or foreground estimation are also used for the determination of anomalous events in [14,51].…”

Section: Deep Learningmentioning

confidence: 99%

Anomaly Detection in Video Sequence With Appearance-Motion Correspondence

Nguyen

Meunier²

2019

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

323

148

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Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-ofthe-art methods. AbstractThis supplementary material provides these contents:• ROC curves of our frame-level scores on the CUHK Avenue and UCSD Ped2 datasets, and Precision-Recall (PR) curves on the traffic datasets.

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