Detecting and Recognizing Salient Object in Videos

Abstract

Saliency detection has been an interesting research field. Some researchers consider it as a segmentation problem some others treat it differently. In this paper, we propose a novel video saliency framework that detects and recognizes the object of interest.

Starting from the assumption that spatial and temporal information of an input video frame can provide better saliency results than using each information alone, we propose a spatio-temporal saliency model for detecting salient objects in videos. First, spatial saliency is measured at patch-level by fusing local contrasts with spatial priors to label each patch as a foreground or a background one. Then, the newly proposed motion distinctiveness feature and temporal gradient magnitude measure are used to obtain the temporal saliency maps. Spatial and temporal saliency maps are fused together into one master saliency map.

Object classification framework contains training and testing stage. On the training phase, we use a convolutional neural network to extract features of the proposed training set. Then, deep features are fed into a Support Vector Machine classifier to produce a classification Model. This model will be used to predict the class of the salient object.

Despite the framework is simple to implement and efficient to run, it has shown good performances and achieved good results.

Experiments on two standard benchmark datasets for video saliency have shown that the proposed temporal cues improve saliency estimation results. Results are compared to six state-of-the-art methods on two benchmark datasets.