AIBM: Accurate and Instant Background Modeling for Moving Object Detection

Kim, Woo Jin; Hwang, Sangwon; Lee, Junhyeop; Woo, Sungmin; Lee, Sangyoun

doi:10.1109/tits.2021.3090092

Cited by 12 publications

(4 citation statements)

References 64 publications

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“…The approach uses temporal difference motion difference and partial spatial motion information. Kim et al [15] develops a model for moving object detection based on instant background modelling. The approach uses Spatio-temporal information and instant background modelling which consists of inpainting and super pixels to enhance the fine details in the images.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Shivakumara

et al. 2023

AIA

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Arbitrary moving object detection including vehicles and human beings in the real environment, such as protected and sensitive areas, is challenging due to arbitrary deformation and directions caused by Shakey camera and wind. This work aims at adopting a Spatio-temporal approach for classifying arbitrarily moving objects. The proposed method segments foreground objects from the background using the frame difference between the median frame and individual frames. This step outputs several different foreground information. The mean of foreground images is computed, which is referred to as the mean activation map. For the mean activation map, the method employs the Fast Fourier Transform (FFT), which outputs amplitude and frequencies. The mean of frequencies is computed for moving objects in using activation maps of temporal frames, which is considered as a frequency feature vector. The features are normalized to avoid the problems of imbalanced features and class sizes. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of arbitrary moving and static videos. For evaluating the proposed method, we construct our dataset, which contains videos of static and arbitrarily moving objects caused by shaky cameras and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance.

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Section: Related Workmentioning

confidence: 99%

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Shivakumara

et al. 2023

AIA

View full text Add to dashboard Cite

show abstract

“…The approach uses temporal difference motion difference and partial spatial motion information. Kim et al (2022) developed a model for moving object detection based on instant background modeling. The approach uses spatiotemporal information and instant background modeling which consists of inpainting and super pixels to enhance the fine details in the images.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Asadzadehkaljahi

Halder

Pal

et al. 2023

AIA

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Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatiotemporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera is inconsistent and unstable, while, for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples, and the random forest classifier is used for the final classification of frames with static objects and arbitrarily moving objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

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“…Illumination challenges, sometimes the Illumination in a scene changes which means that the background color in each frame may be affected and also this could cause some objects to disappear due to changing Illumination [17].…”

Section: Some Challenges In Video Object Detectionmentioning

confidence: 99%

“…It could be one of the obstacles [18,19,20]. The significant challenging point in object detection is occlusion and it may be full/partial and can occur anytime an object passes behind another object [17,21].…”

Section: Some Challenges In Video Object Detectionmentioning

confidence: 99%

Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques

et al. 2022

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Traffic safety aims to change the attitude of citizens towards careless traffic on the roads, making this the first step towards changing behavior. Also, teach the rules of safe pedestrian behavior and minimize the risks of road accidents. So many regulations have been set to avoid road accidents and traffic jams, which is the study scope of this paper using IT technology. With the expanding interests in Computer vision use cases such as vehicles self-driving, face recognition, intelligent transportation frameworks and so on individuals are hoping to assemble custom AI models to recognize and distinguish specific objects. Object detection is part of a computer's vision where objects that can be observed externally and are found in videos can be identified and tracked by computers. Therefore, object tracking is an important part of video analysis. There are many proposed methods such as Tracking, Learning, Detection, Mean shift and MIL. In this paper, the computer vision state in object detecting domain along with its challenges are discussed, also we address some requirements and techniques to overcome these challenges. Finally, TensorFlow technology is presented as a recommended solution to support Lane’s violation.

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AIBM: Accurate and Instant Background Modeling for Moving Object Detection

Cited by 12 publications

References 64 publications

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Spatio-Temporal FFT Based Approach for Arbitrarily Moving Object Classification in Videos of Protected and Sensitive Scenes

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques

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