Automated Censoring of Cigarettes in Videos Using Deep Learning Techniques

Dhanwal, Swapnil; Bhaskar, Vishnu; Agarwal, Tanya

doi:10.1007/978-981-15-3643-4_26

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The authors suggested that their system could be useful in a variety of settings, such as hospitals, schools, and public spaces, to detect smoking activity and help enforce smoking bans. Another study that considered smoking content was conducted by Dhanwal et al (2020). The authors stated that videos with smoking content had negative effects, especially for children.…”

Section: Related Workmentioning

confidence: 99%

DeepCens: A deep learning‐based system for real‐time image and video censorship

Yuksel,

Tan

2023

Expert Systems

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The popularity of social networks and video‐on‐demand platforms has increased the importance of image and video censorship. These platforms contain various content such as violence, explicit content, drug use and smoking that may be offensive or harmful to certain viewers. As a result, censorship is employed to filter or remove content that is unsuitable for particular audiences such as children and teenagers. However, policies for censoring harmful content in digital environments are either limited or nonexistent. This underscores the need for automated systems that can detect and censor harmful content in real time. To address these challenges, we developed the first of our knowledge systems using deep learning techniques to censor harmful content. We propose two novel, YOLO‐based real‐time censorship algorithms. Our approaches employ a pipeline‐based architecture that parallelizes the operations with subprocesses. In our experiments, the proposed algorithms performed faster and with higher accuracies compared to traditional approaches. Specifically, its content‐based accuracies were 98% for explicit content, 97% for alcohol, 98% for cigarettes and 97% for violence. Our research highlights the importance of developing effective and efficient solutions for censoring harmful content on digital media platforms. Our deep learning‐based system represents a promising approach to this challenge and has the potential to enhance user safety and protect vulnerable groups from harmful and offensive content. Future research will continue to refine and improve such systems to better address the evolving landscape of digital media and the challenges posed by harmful content.

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Section: Related Workmentioning

confidence: 99%

DeepCens: A deep learning‐based system for real‐time image and video censorship

Yuksel,

Tan

2023

Expert Systems

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“…At the same time, sheer volume of videos and images generated these days necessitate the need of an automatic action recognition system. Action recognition is useful in many applications including real-time surveillance [2][3], crowd behavior monitoring [4] [5], bio-mechanical analysis of actions for sports and medicine [6] [7], anomaly detection [8] [9], automatic video organization / tagging [10] [11], health care [12] [13], elder care [14] [15], child/animal monitoring [16] and video censoring [17].…”

Section: Introductionmentioning

confidence: 99%

Predicting Actions in Videos and Action-Based Segmentation Using Deep Learning

et al. 2021

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In this paper, we propose a technique to recognize multiple actions in a video using deep learning. The proposed approach is concerned with interpreting the overall context of a video and transforming it into one or more appropriate actions. In order to cope with multiple actions in a video, our proposed technique first determines the individual segments/shots in a video using intersections of color histograms. The segmented parts are then fed to the action recognition system comprising a combination of a Convolution Neural Network (CNN) and a Long-Short-Term Memory (LSTM) network trained on our action vocabulary. The segments are then labeled according to their predicted actions and a compact set of distinct actions is produced. Using the corpus generated by the shot detection phase, which includes the location of key frames in shots, and start/end timestamps of a shot, we can also perform video segmentation based on an action query. Hence, the proposed technique can be used for a number of tasks such as content censoring, on-demand scene retrieval, video summarizing, and query based scene/video retrieval, to name a few. The proposed technique also stands aprat from the existing approaches which either do not take into account the motion information for action prediction or do not perform action-based video segmentation. The experimental results presented in this paper show that the proposed technique not only finds the the complete set of actions present in the video, but can also find all the relevant parts in a video based on an action query.

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