Transfer learning for video anomaly detection

Bansod, Suprit D.; Nandedkar, Abhijeet V.

doi:10.3233/jifs-169908

Cited by 35 publications

(24 citation statements)

References 16 publications

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“…Generally, VGGNet variants have the best results and they are considered as suitable models for abnormal detection since they pre-trained on similar images such as bicycles, small carts, cars, and pedestrians. Table 5 presents a comparison between the proposed framework with other contemporary frameworks [17], [61]- [67] using UMN dataset in terms of AUC and accuracy. Note that, the proposed framework provides the best value of AUC and very competitive value of accuracy to Sabokrou et al [67].…”

Section: B Discussionmentioning

confidence: 99%

“…Therefore, applying deep learning models is quite challenging. In order to overcome this challenge, the concept of transfer learning has been introduced [16], [17], which means that the CNNs networks that trained on a particular dataset or certain mission may be fine-tuned for a new mission even if the scope is different. Because of the rapid increase in the amount of published information or data (e.g., audios and videos), demand for high values on accuracy and computational efficiency is also increased.…”

Section: Theoretical Background a Transfer Learning Backgroundmentioning

confidence: 99%

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Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

2020

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Today, machine learning and deep learning have paved the way for vital and critical applications such as abnormal detection. Despite the modernity of transfer learning, it has proved to be one of the crucial inventions in the field of deep learning because of its promising results. For the purpose of this study, transfer learning is utilized to extract human motion features from RGB video frames to improve detection accuracy. A convolutional neural network (CNN) based on Visual Geometry Group network 19 (VGGNet-19) pre-trained model is used to extract descriptive features. Next, the feature vector is passed into Binary Support Vector Machine classifier (BSVM) to construct a binary-SVM model. The performance of the proposed framework is evaluated by three parameters: accuracy, area under the curve, and equal error rate. Experiments performed on two different datasets comprising highly different context abnormalities accomplished an accuracy of 97.44% and an area under the curve (AUC) of 0.9795 for University of Minnesota (UMN) dataset and accomplished an accuracy of 86.69% and an AUC of 0.7987 for University of California, San Diego Pedistrain1 (UCSD-PED1) dataset. Moreover, the performance of the pre-trained network VGGNet-19 with handcrafted feature descriptors and with other CNN pre-trained networks, respectively, has been investigated in this study for abnormal behavior detection. The results demonstrated that VGGNet-19 has better performance than histogram of oriented gradients, background subtraction, and optical flow. In addition, the VGGNet-19 shows higher detection accuracy than other pre-trained networks: GoogleNet, ResNet50, AlexNet, and VGGNet-16. INDEX TERMS Abnormal behavior detection, transfer learning, convolutional neural network, VGGNet-19, handcrafted feature descriptors, pre-trained networks.

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Section: B Discussionmentioning

confidence: 99%

Section: Theoretical Background a Transfer Learning Backgroundmentioning

confidence: 99%

Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

2020

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“…Moreover, these data are usually not labeled at all, or only a small part is labeled. Meanwhile, we can use a fully labeled data set collected and annotated by the University of California and San Diego (UCSD) [9,10] as the source domain data to help the training of the target domain model. However, the video of UCSD is usually captured on the campus of the university, which is different from the target domain's locations, such as shopping centers and subway stations.…”

Section: Remarkmentioning

confidence: 99%

Learning Transferable Convolutional Proxy by SMI-Based Matching Technique

Jin

Jia

2020

Shock and Vibration

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Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.

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“…By reading the content of the article which cites the others, we can annotate the "knowledge flowing'' if one work is inspired or a following work of the other research works. However, such annotation by reading is time-consuming and subject to individual annotators, thus it is very necessary to develop an automatic system to estimate the knowledge flow from the article citation network [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Estimating Network Flowing over Edges by Recursive Network Embedding

Wang

2020

Shock and Vibration

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In this paper, we propose a novel semisupervised learning framework to learn the flows of edges over a graph. Given the flow values of the labeled edges, the task of this paper is to learn the unknown flow values of the remaining unlabeled edges. To this end, we introduce a value amount hold by each node and impose that the amount of values flowing from the conjunctive edges of each node to be consistent with the node’s own value. We propose to embed the nodes to a continuous vector space so that the embedding vector of each node can be reconstructed from its neighbors by a recursive neural network model, linear normalized long short-term memory. Moreover, we argue that the value of each node is also embedded in the embedding vectors of its neighbors, thus propose to approximate the node value from the output of the neighborhood recursive network. We build a unified learning framework by formulating a minimization problem. To construct the learning problem, we build three subproblems of minimization: (1) the embedding error of each node from the recursive network, (2) the loss of the construction for the amount of value of each node, and (3) the difference between the value amount of each node and the estimated value from the edge flows. We develop an iterative algorithm to learn the node embeddings, edge flows, and node values jointly. We perform experiments based on the datasets of some network data, including the transportation network and innovation. The experimental results indicate that our algorithm is more effective than the state-of-the-arts.

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Transfer learning for video anomaly detection

Cited by 35 publications

References 16 publications

Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

Learning Transferable Convolutional Proxy by SMI-Based Matching Technique

Estimating Network Flowing over Edges by Recursive Network Embedding

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