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A Study on a 3D Convolution-Based Video Recognition System for Driving Aggressiveness Recognition
http://doi.org/10.5626/JOK.2024.51.12.1094
This study aims to develop and test a model for classifying driving styles and recognizing driving aggressiveness using video data collected from a vehicle's front camera. To achieve this, the CARLA simulator was employed to simulate aggressive and cautious driving behaviors across various road environments, while a 3D convolution-based VideoResNet model was utilized for analyzing the video data. The results showed that the trained model achieved high accuracy in classifying driving styles during urban driving scenarios, demonstrating the effectiveness of front camera data in recognizing driving aggressiveness. Furthermore, experiments confirmed the model's capability to classify driving styles in an online manner, highlighting its potential as an on-the-spot tool for recognizing driving aggressiveness. Additionally, this study investigated the effect of road environments and speed variations on aggressiveness scores, demonstrating that the model can effectively consider the interplay between road complexity and speed when makingin its predictions.
Deep Learning-Based Abnormal Event Recognition Method for Detecting Pedestrian Abnormal Events in CCTV Video
Jinha Song, Youngjoon Hwang, Jongho Nang
http://doi.org/10.5626/JOK.2024.51.9.771
With increasing CCTV installations, the workload for monitoring has significantly increased. However, a growing workforce has reached its limits in addressing this issue. To overcome this problem, intelligent CCTV technology has been developed. However, this technology experiences performance degradation in various situations. This paper proposes a robust and versatile method for integrated abnormal behavior recognition in CCTV footage that could be applied in multiple situations. This method could extract frame images from videos to use raw images and heatmap representation images as inputs. It could remove feature vectors through merging methods at both image and feature vector levels. Based on these vectors, we proposed an abnormal behavior recognition method utilizing 2D CNN models, 3D CNN models, LSTM, and Average Pooling. We defined minor classes for performance validation and generated 1,957 abnormal behavior video clips for testing. The proposed method is expected to improve the accuracy of abnormal behavior recognition through CCTV footage, thereby enhancing the efficiency of security and surveillance systems.
A Fusion of CNN-based Frame Vector for Segment-level Video Partial Copy Detection
http://doi.org/10.5626/JOK.2021.48.1.43
Recently, the demand for media has grown rapidly, led by multimedia content platforms such as YouTube and Instagram. As a result, problems such as copyright protection and the spread of illegal content have arisen. To solve these problems, studies have been proposed to extract unique identifiers based on the content. However, existing studies were designed for simulated transformation and failed to detect whether the copied videos were actually shared. In this paper, we proposed a deep learning-based segment fingerprint that fused frame information for partial copy detection that was robust for various variations in the actually shared video. We used TIRI for data-level fusion and Pooling for feature-level fusion. We also designed a detection system with a segment fingerprint that was trained with Triplet loss. We evaluated the performance with VCDB, a dataset collected based on YouTube, and obtained 66% performance by fusing frame features sampled for 5 seconds with Max pooling for detecting video partial-copy problems.
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