Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance
Sirine Ammar, Thierry Bouwmans, Nizar Zaghden, Mahmoud Néji
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.