Anomaly Detection Using Motion Patterns Computed from Optical Flow

Parvathy, R; Thilakan, Soumya; Joy, Meenu; Sameera, K. M.

doi:10.1109/icacc.2013.18

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…However, as the number of IP cameras increases explosively, a fully automatic video recognition framework becomes essential, replacing a manual monitoring. Many algorithms [4], [5], [13]- [15] have been developed to handle vast amounts of data automatically. These algorithms can be used for the video recognition in a cloud server.…”

Section: Background and Related Work A Edge Computingmentioning

confidence: 99%

“…Previously, the optical flows were computed directly from the video frames [4]. However, recently, the optical flow computations have been replaced by neural networks.…”

Section: B Neural Network For Videosmentioning

confidence: 99%

“…Anomaly detection in a video is one of the challenging problems and has been studied for a long time in the computer vision community. The traditional anomaly detection mainly relied on the motion information between two consecutive frames extracted by optical flow [4] or dynamic Bayesian Network (DBN) [5]. Recently, deep neural networks (DNN) have been exploited to learn motion information in a video.…”

Section: Introductionmentioning

confidence: 99%

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Deep Edge Computing for Videos

Kim

Won³

2021

IEEE Access

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This paper provides a modular architecture with deep neural networks as a solution for realtime video analytics in an edge-computing environment. The modular architecture consists of two networks of Front-CNN (Convolutional Neural Network) and Back-CNN, where we adopt Shallow 3D CNN (S3D) as the Front-CNN and a pre-trained 2D CNN as the Back-CNN. The S3D (i.e., the Front CNN) is in charge of condensing a sequence of video frames into a feature map with three channels. That is, the S3D takes a set of sequential frames in the video shot as input and yields a learned 3 channel feature map (3CFM) as output. Since the 3CFM is compatible with the three-channel RGB color image format, we can use the output of the S3D (i.e., the 3CFM) as the input to a pre-trained 2D CNN of the Back-CNN for the transfer-learning. This serial connection of Front-CNN and Back-CNN architecture is end-to-end trainable to learn both spatial and temporal information of videos. Experimental results on the public datasets of UCF-Crime and UR-Fall Detection show that the proposed S3D-2DCNN model outperforms the existing methods and achieves state-of-the-art performance. Moreover, since our Front-CNN and Back-CNN modules have a shallow S3D and a light-weighted 2D CNN, respectively, it is suitable for real-time video recognition in edge-computing environments. We have implemented our CNN model on NVIDIA Jetson Nano Developer as an edge-computing device to show its real-time execution.

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Section: Background and Related Work A Edge Computingmentioning

confidence: 99%

“…Previously, the optical flows were computed directly from the video frames [4]. However, recently, the optical flow computations have been replaced by neural networks.…”

Section: B Neural Network For Videosmentioning

confidence: 99%