Moving Object Detection System Based on the Modified Temporal Difference and OTSU algorithm

Boufares, Oussama; Boussif, Mohamed; Aloui, Noureddine

doi:10.1109/ssd52085.2021.9429516

Cited by 7 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To show effectiveness of the proposed method, we implemented three state-of-the-art methods, namely Boufares et al (2021), Wang et al (2021) that use temporal difference and OTSU thresholding for moving object detection in the video, and Rahiminezhad et al (2022) which propose subtraction approach for moving object detection in video, for comparative study. The reason to choose the above methods is that the objective of the methods is the same as the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…Since these methods are relevant to arbitrary object movements detection, we review the latest papers. Boufares et al (2021) proposed a method for moving object detection using temporal difference and OTSU thresholding technique. The approach uses input frame difference at pixels level for detecting moving objects in the videos.…”

Section: Related Workmentioning

confidence: 99%

“…In the case of Figure 1(a), due to shaky camera, the frames are blurred while in Figure 1(b), due to indoor scenes, the objects in the frames are not visible. There are number of methods proposed in the literature for static and moving object detection (Boufares et al, 2021;Wang et al, 2021). However, these methods focus on the video captured in daytime with high quality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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 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

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Ada berbagai macam metode untuk mendeteksi suatu objek yang bergerak melalui sebuah citra, dimulai dari background subtraction (Jusman et al, 2020;Zhou & Maskell, 2017), frame difference (Jusman et al, 2020;Nakashima & Yabuta, 2018), temporal differencing (Boufares et al, 2021), dan optical flow (J. Zhang et al, 2019).…”

Section: Pendahuluanunclassified

Line Crossing Detector System Pada Real-Time Situational Awareness Dengan Menggunakan Spatial Sample Difference Consensus

Meldiyana

Fathina

Yuner

et al. 2022

JTT

View full text Add to dashboard Cite

Pengawasan melalui CCTV merupakan salah satu antisipasi ancaman pada VIP dan atau VVIP. Namun pengawasan menggunakan CCTV ini memungkinkan luputnya pengawasan CCTV oleh aparatur. CCTV hanya mampu merekam, namun tidak memiliki kemampuan real-time situational awareness. Line crossing detector merupakan salah satu produk real-time situational awareness yang mampu memantau suatu key area atau area tertentu yang telah ditentukan sebagai area pengawasan (clear area). Sistem line crossing detector ini hanya akan diimplementasikan di dalam ruangan (indoor area). Secara khusus sistem ini dapat bekerja secara optimal pada tempat yang tidak padat aktivitas. Sistem line crossing detector menggunakan Spatial Sample Difference Consensus (SSDC) untuk mendeteksi adanya objek bergerak. Selain itu, Line Crossing Detector memiliki kemampuan untuk melakukan proses object tracking menggunakan centroid tracking dan mengidentifikasi objek yang masuk ke dalam area pengawasan menggunakan model YOLO V3. Melalui metode student t, sistem mampu mendeteksi objek yang melewati garis pengawasan dengan tingkat kepercayaan 90% dengan tingkat signifikansi 10%.

show abstract

“…1(b) that the frames are suffering from poor quality and the objects are not visible properly including human movements. There are several methods proposed in the literature for static and moving object detection [1][2][3][4][5][6][7][8]. However, these methods focus on the video captured in the day with high quality.…”

Section: Introductionmentioning

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

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.

show abstract

Moving Object Detection System Based on the Modified Temporal Difference and OTSU algorithm

Cited by 7 publications

References 14 publications

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

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

Line Crossing Detector System Pada Real-Time Situational Awareness Dengan Menggunakan Spatial Sample Difference Consensus

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

Contact Info

Product

Resources

About