Video classification for efficient data storage using deep learning: a comparison of sequential and simultaneous feature extraction methods.
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| Title: | Video classification for efficient data storage using deep learning: a comparison of sequential and simultaneous feature extraction methods. |
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| Authors: | Dang Thi, Phuc1 (AUTHOR) phucdt@iuh.edu.vn, Nguyen Dang, Hoang Thi1 (AUTHOR) thinguyen280601@gmail.com, Doan Huu, Phuc1 (AUTHOR) phuc1032000@gmail.com, Dau Sy, Hieu2 (AUTHOR) dausyhieu@hcmut.edu.vn |
| Source: | Multimedia Tools & Applications. Feb2025, Vol. 84 Issue 6, p3071-3094. 24p. |
| Subjects: | Transformer models, Camcorders, Artificial intelligence, Cognitive psychology, Sequential learning, Deep learning, Object recognition (Computer vision) |
| Abstract: | Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show that models from Approach 1 exhibit excellent feature extraction capabilities, effectively classifying activities with high accuracy. Notably, InceptionV3+LSTM and VGG16+ViT models achieve accuracy rates exceeding 93%. Conversely, models from Approach 2 show fast training speeds and lightweight model sizes but struggle with activity classification, resulting in lower accuracy. To meet the system's requirements for high accuracy and real-time performance, we select the InceptionV3+LSTM model for deployment. This model is further fine-tuned to achieve better model size and training speed compared to other models in this approach. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show that models from Approach 1 exhibit excellent feature extraction capabilities, effectively classifying activities with high accuracy. Notably, InceptionV3+LSTM and VGG16+ViT models achieve accuracy rates exceeding 93%. Conversely, models from Approach 2 show fast training speeds and lightweight model sizes but struggle with activity classification, resulting in lower accuracy. To meet the system's requirements for high accuracy and real-time performance, we select the InceptionV3+LSTM model for deployment. This model is further fine-tuned to achieve better model size and training speed compared to other models in this approach. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13807501 |
| DOI: | 10.1007/s11042-024-20549-5 |
