Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance

Abstract

<jats:title>Abstract</jats:title> <jats:p>The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating features for violence activity classification with abrupt camera motion. This research work investigated deep representation models using transfer learning for handling the issue of abrupt camera motion. Consequently, a novel deep multi-net (DMN) architecture based on AlexNet and GoogleNet is proposed for violence detection in videos. AlexNet and GoogleNet are top-ranked pre-trained models for image classification with distinct pre-learnt potential features. The fusion of these models can yield superior performance. The proposed DMN unleashed the integrated potential by concurrently coalescing both networks. The results confirmed that DMN outperformed state-of-the-art methods by learning finest discriminating features and achieved 99.82% and 100% accuracy on hockey and movies data sets, respectively. Moreover, DMN has faster learning capability i.e. 1.33 and 2.28 times faster than AlexNet and GoogleNet, which makes it an effective learning architecture on images and videos.</jats:p>

Violence behavior recognition of two-cascade temporal shift module with attention mechanism

“…2 According to the method of feature extraction, behavior recognition can be divided into traditional behavior recognition 3,4 and behavior recognition based on deep learning. [5][6][7][8] Traditional behavior recognition methods mainly extract features manually, and the types of features mainly include global features and local features. The global feature extraction mainly includes two methods: silhouette and human joint points.…”

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

“…This implies that it was possible to combine or compare the measurement results from the described research with the measurement results in this study, ultimately leading to an accurate and reliable evaluation of the novel model. Besides building intelligent applications on the WESAD data set, many independent attempts were made to analyze emotions and extract meaningful insights from collected data of emotional parameters [20][21][22]. In [20,21], different techniques were used to recognize various emotions, understand these emotions, and understand the overall reasons for their occurrence.…”

“…In [20,21], different techniques were used to recognize various emotions, understand these emotions, and understand the overall reasons for their occurrence. Additionally, in [22], the Deep Multi-Net CNN Model was used for violence recognition in video surveillance. In this paper, another emotional state that caused violent behavior was examined, but not by using internal human conditions and measurement of biological parameters, but by using recorded participants' video shots.…”

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“…The italic text refers to the currently observed topic. detect violent and non-violent situations using the collected data [51] VD using soft computing techniques techniques from video data is in practice since decades [15], initiated based on traditional image processing techniques. The early VD literature has consideration of various attributes for decision making such as acceleration of human motion, their appearance, and motion flow, among many others.…”