Bidirectional Convolutional LSTM for the Detection of Violence in Videos

Hanson, Alex; Pnvr, Koutilya; Krishnagopal, Sanjukta; Davis, Larry S.

doi:10.1007/978-3-030-11012-3_24

Cited by 114 publications

(70 citation statements)

References 27 publications

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“…The input, which is the concatenation of all recursive inference block outputs according to the sequence, is denoted as [ , , … , ]. ConvLSTM [49] and BiConvLSTM [50] are usually used to learn global, long-term spatiotemporal features of videos. In our approach, we considered the recursions of RIB as a temporal sequence in the order of the recursion.…”

Section: Biconvlstmmentioning

confidence: 99%

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Chang

Luo

2019

Remote Sensing

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Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task.

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Section: Biconvlstmmentioning

confidence: 99%

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Chang

Luo

2019

Remote Sensing

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“…Recent studies have focused on the detection of violence or fighting among multiple individuals in a crowd, with particular emphasis on violent scenes that cannot be effectively detected by the security personnel in the field. Towards this objective, the latest advances in deep learning have been exploited, whereby the temporal analysis of visual information is almost always performed using Convolutional Neural Networks (CNNs) [4], Recurrent Neural Networks (RNNs) [8], or 3D Convolutional Neural Networks (3D-CNNs) [22].…”

Section: Introductionmentioning

confidence: 99%

A Crowd Analysis Framework for Detecting Violence Scenes

Gkountakos

Ioannidis

Tsikrika

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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This work examines violence detection in video scenes of crowds and proposes a crowd violence detection framework based on a 3D convolutional deep learning architecture, the 3D-ResNet model with 50 layers. The proposed framework is evaluated on the Violent Flows dataset against several state-of-the-art approaches and achieves higher accuracy values in almost all occasions, while also performing the violence detection activities in (near) real-time.

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“…For many applications, utilising temporal consistency has been demonstrated to improve performance. Examples include depth estimation [39], motion segmentation [4], action recognition [22], super resolution [17] and superpixel segmentation [33]. Single image approaches for horizon line estimation may do gross mistakes when the image provides few or misleading clues.…”

Section: Introductionmentioning

confidence: 99%

Temporally Consistent Horizon Lines

Kluger

Ackermann

Yang

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision. For instance, in navigation of autonomous vehicles or driver assistance, it can be used to improve 3D reconstruction as well as for semantic interpretation of dynamic environments. While both algorithms and datasets exist for single images, the problem of horizon line estimation from video sequences has not gained attention. In this paper, we show how convolutional neural networks are able to utilise the temporal consistency imposed by video sequences in order to increase the accuracy and reduce the variance of horizon line estimates. A novel CNN architecture with an improved residual convolutional LSTM is presented for temporally consistent horizon line estimation. We propose an adaptive loss function that ensures stable training as well as accurate results. Furthermore, we introduce an extension of the KITTI dataset which contains precise horizon line labels for 43699 images across 72 video sequences. A comprehensive evaluation shows that the proposed approach consistently achieves superior performance compared with existing methods.

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Bidirectional Convolutional LSTM for the Detection of Violence in Videos

Cited by 114 publications

References 27 publications

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution

A Crowd Analysis Framework for Detecting Violence Scenes

Temporally Consistent Horizon Lines

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