Investigation and Improvement of VGG based Encoder-Decoder Architecture for Background Subtraction
Rinku Rabidas, Dheeraj Kr. Ravi, Shashikant Pradhan, Rhittwikraj Moudgollya, Amrita Ganguly · 2020
Object detection in motion pictures is always a challenging task due to the presence of dynamic background. Deep learning architectures especially encoder-decoder type has shown promising performance in segmenting foreground objects against the background in video sequences. Thus, in this work, a VGG-16 based encoder-decoder architecture is investigated and several modifications are proposed to improve the efficiency the model. The modified models are evaluated on two different standard databases- CDNet 2014 and SBI2015 with various scenes and achieved the highest precision of 0.99 which is competitive in nature with the current schemes in the state-of-the-art.