A comparison between a DTCNN and SOM like approach for dynamic object detection in videos

In this paper a DTCNN model for dynamic object segmentation in videos is presented. The proposed method involves three main stages; dynamic background registration, dynamic objects detection and object segmentation improvement. Two DTCNNs are used, one to achieved object detection and other for morphologic operations in order to improve object segmentation. Visual and quantitative results are compared with findings of a Self-organizing map SOM-like dynamic object detection approach. Considering the experiments reported, it can be said that the proposed method shows acceptable results with some improvements over the SOM because the DTCNN method does not need human intervention for parameter adjustment.