Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance. Issue 8 (13th May 2020)

• Record Type: Journal Article
• Authors: Ammar, Sirine; Bouwmans, Thierry; Zaghden, Nizar; Neji, Mahmoud
• Is Part Of: IET image processing. Volume 14:Issue 8(2020)
• Page Start: 1490
• Page End: 1501
• Publication Date: 2020-05-13
• Subjects: image classification -- image motion analysis -- video surveillance -- object detection -- image segmentation -- image sequences -- learning (artificial intelligence) -- video signal processing; video surveillance -- moving objects -- video sequences -- unsupervised anomaly discovery framework -- generative adversarial networks -- deep detector classifier -- spatial context -- temporal context -- foreground object segmentation -- generative models -- segmented objects -- videos surveillance -- missing data imputation task; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0769
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the spatial and temporal context in order to perform foreground objects segmentation. For post‐processing, some morphological operations are considered to better segment and extract the desired objects. Finally, they take advantage of the power of generative models, which recognise the problem of semi‐supervised learning as a specific missing data imputation task in order to classify the segmented objects. They evaluate the method with multiple datasets and the results confirm the effectiveness of the proposed approach, which achieves superior performance over the state‐of‐the‐art methods having the capabilities of segmenting and classifying moving objects from videos surveillance.