Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation

Kim, Haram; Kim, Pyojin; Kim, H. Jin

doi:10.1109/icra40945.2020.9196767

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…To verify the performance of our method, we compared the accuracy of our algorithm with the state-of-the-art VO systems that can run with stereo RGB data. The selected methods were stvo pl [27], ORB SLAM2 [6] and BoPLW [35]. In order to fairly compare all methods, we only kept the front end of ORB SLAM2 and BoPLW to form VO systems.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes

Zhang¹

2022

Preprint

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In this paper, we consider the problems in the practical application of visual simultaneous localization and mapping (SLAM). With the popularization and application of the technology in wide scope, the practicability of SLAM system has become a new hot topic after the accuracy and robustness, e.g., how to keep the stability of the system and achieve accurate pose estimation in the low-texture and dynamic environment, and how to improve the universality and real-time performance of the system in the real scenes, etc. This paper proposes a real-time stereo indirect visual SLAM system, PLD-SLAM, which combines point and line features, and avoid the impact of dynamic objects in highly dynamic environments. We also present a novel global gray similarity (GGS) algorithm to achieve reasonable keyframe selection and efficient loop closure detection (LCD). Benefiting from the GGS, PLD-SLAM can realize realtime accurate pose estimation in most real scenes without pretraining and loading a huge feature dictionary model. To verify the performance of the proposed system, we compare it with existing state-of-the-art (SOTA) methods on the public datasets KITTI, EuRoC MAV, and the indoor stereo datasets provided by us, etc. The experiments show that the PLD-SLAM has better real-time performance while ensuring stability and accuracy in most scenarios. In addition, through the analysis of the experimental results of the GGS, we can find it has excellent performance in the keyframe selection and LCD.

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Section: Experimental Results and Analysismentioning

confidence: 99%

PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes

Zhang¹

2022

Preprint

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“…Therefore, the developed system can be used for both protecting areas and saving people's life. There are several methods for object detection, object tracing, intruder detection in the video, etc., in the literature (Arjun et al, 2020;Chandra and Panda, 2021;Kim et al, 2020;Lenac et al, 2021;Ramesh et al, 2021;Vndana et al, 2022). However, most of the existing methods focus on detecting particular object in the day video for particular applications but not generalized methods and for different applications.…”

Section: Introductionmentioning

confidence: 98%

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.

show abstract

“…Therefore, the developed system can be used for both protecting areas and saving people's life. There are several methods for object detection, object tracing, intruder detection in the video, etc., in the literature (Arjun et al, 2020;Chandra & Panda, 2021;Kim et al, 2020;Lenac et al, 2021;Ramesh et al, 2021;Vndana et al, 2022). However, most of the existing methods focus on detecting particular object in the day video for particular applications but not generalized methods and for different applications.…”

Section: Introductionmentioning

confidence: 98%

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

Asadzadehkaljahi

Halder

Pal

et al. 2023

AIA

View full text Add to dashboard Cite

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 spatio-temporal 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 are 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 arbitrary movement 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.

show abstract

Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation

Cited by 7 publications

References 17 publications

PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes

PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes

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

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

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