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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 Generation Method of Segment-level Fingerprint-based Transformer for Video Partial Copy Detection
Sooyeon Kang, Minsoo Jeong, Jongho Nang
http://doi.org/10.5626/JOK.2023.50.3.257
With the recent generalization of video-capturing devices and the development of various multimedia platforms, video content usage is increasing every year. However, as a side effect of this, copyright infringement crimes regarding video content are also increasing. In this paper, we propose a segment fingerprint generation method for robust video copy detection systems in various transforms to address these problems. We propose a method for generating a frame fingerprint with a hybrid vision transformer, weighting the generated frame fingerprint with a transformer encoder, and fusing it into Maxpooling to aggregate a segment fingerprint. We used the VCDB dataset and measured the F1 score, which was 0.772.
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.
A Best View Selection Method in Videos of Interested Player Captured by Multiple Cameras
Hotak Hong, Gimun Um, Jongho Nang
http://doi.org/10.5626/JOK.2017.44.12.1319
In recent years, the number of video cameras that are used to record and broadcast live sporting events has increased, and selecting the shots with the best view from multiple cameras has been an actively researched topic. Existing approaches have assumed that the background in video is fixed. However, this paper proposes a best view selection method for cases in which the background is not fixed. In our study, an athlete of interest was recorded in video during motion with multiple cameras. Then, each frame from all cameras is analyzed for establishing rules to select the best view. The frames were selected using our system and are compared with what human viewers have indicated as being the most desirable. For the evaluation, we asked each of 20 non-specialists to pick the best and worst views. The set of the best views that were selected the most coincided with 54.5% of the frame selection using our proposed method. On the other hand, the set of views most selected as worst through human selection coincided with 9% of best view shots selected using our method, demonstrating the efficacy of our proposed method.
A Personal Video Event Classification Method based on Multi-Modalities by DNN-Learning
In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.
A Post-Verification Method of Near-Duplicate Image Detection using SIFT Descriptor Binarization
In recent years, as near-duplicate image has been increasing explosively by the spread of Internet and image-editing technology that allows easy access to image contents, related research has been done briskly. However, BoF (Bag-of-Feature), the most frequently used method for near-duplicate image detection, can cause problems that distinguish the same features from different features or the different features from same features in the quantization process of approximating a high-level local features to low-level. Therefore, a post-verification method for BoF is required to overcome the limitation of vector quantization. In this paper, we proposed and analyzed the performance of a post-verification method for BoF, which converts SIFT (Scale Invariant Feature Transform) descriptors into 128 bits binary codes and compares binary distance regarding of a short ranked list by BoF using the codes. Through an experiment using 1500 original images, it was shown that the near-duplicate detection accuracy was improved by approximately 4% over the previous BoF method.
Design of a Video Metadata Schema and Implementation of an Authoring Tool for User Edited Contents Creation
In this paper, we design new video metadata schema for searching video segments to create UEC (User Edited Contents). The proposed video metadata schema employs hierarchically structured units of ‘Title-Event-Place(Scene)-Shot’, and defines the fields of the semantic information as structured form in each segment unit. Since this video metadata schema is defined by analyzing the structure of existing UECs and by experimenting the tagging and searching the video segment units for creating the UECs, it helps the users to search useful video segments for UEC easily than MPEG-7 MDS (Multimedia Description Scheme) which is a general purpose international standard for video metadata schema.
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