DEARESt: Deep Convolutional Aberrant Behavior Detection in Real-world Scenarios

Biradar, Kuldeep M.; Dube, Sachin; Vipparthi, Santosh Kumar

doi:10.1109/iciinfs.2018.8721378

Cited by 9 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We achieved overall accuracies of 78.43%, 98.20%, and 98.80%, which is increased by 1.77%, 0.76%, and 8.62% when compared to existing state-of-the-art techniques, with fewer parameters and a reduced model, as shown in Table 3 . The proposed model can process a sequence of 30 frames in 0.263 s, which is comparatively lower than the recent existing techniques [ 47 , 62 , 63 ]. The sizes of existing models are much bigger, and their recognition performance is relatively low as compared to our proposed model, as shown in Table 2 .…”

Section: Resultsmentioning

confidence: 98%

“…The challenging part of the UCF-Crime dataset is that it only contains temporal annotation for the testing set. We follow a former research strategy [ 47 ] to determine the training, testing, and validation ratio. The UMN dataset consists of 11 video sequences of various scenes of abnormal activities and is an extensively utilized dataset.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, the performance of our proposed anomaly recognition model is compared with state-of-the-art techniques by using the UCF-Crime dataset. The authors of [ 47 ] checked various deep learning models, i.e., VGG-16, VGG-19, FlowNet, and DEARESt, respectively. The DEARESt model provides the best performance among these methods.…”

Section: Resultsmentioning

confidence: 99%

“…In anomaly recognition, a delay in response can cost human lives and property; therefore, efficient model selection is a very important aspect for any anomaly recognition system. Our decision to use the light-weight CNN model MobileNetV2 is due to its small storage size, a smaller number of learned parameters, and its fast processing time, with a performance equivalent to heavy-weight CNN models [ 12 , 47 , 60 ]. The efficiency of the proposed model is compared with these existing techniques in terms of model size, time complexity, and the number of parameters, as shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Zhu et al [ 13 ] proposed a temporal augmented network with MIL by incorporating an attention block and achieved state-of-the-art performance for normal and abnormal event detection. Kuldeep et al [ 47 ] suggested a system known as “DEARESt” anomaly recognition. This system is based on two flow feature networks: one uses CNN-based features while the other uses motion features separately.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Ullah

Hussain

et al. 2021

Sensors

View full text Add to dashboard Cite

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

show abstract

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations