This paper presents a new method for accessing the aesthetic quality of videos. It consists of two processes: aesthetic features construction and temporal integration. First, our method combines both photo-based and motion-based visual clues to extract the aesthetic features for each frame in a video. We introduce new motion-based features built from optical flow and salient region extraction, and show their effectiveness to enhance the estimation of aesthetic values. Then, a temporal-order-aware framework that integrates the frame-based features is presented to further improve the evaluation accuracy by taking the time-varying properties into consideration. The experimental results demonstrate that our approach can accomplish remarkable improvement for aesthetic quality assessment of videos.

In this paper, we present a simple but effective method called Contrast-Aware Saliency (CAS) to detect visual saliency by utilizing two general characteristics: rareness and compactness. In our approach, multiple-salient-spots are used to find initial salient clues, which appear to be rare and unique parts in an image. Then, the salient regions are detected by aggregating the surrounding regions of the spots, which fulfil the compactness nature of salient objects. Experimental results show the proposed CAS performs well in the benchmark dataset.

This paper aims to accomplish the work of assessing the aesthetic quality of a video. Unlike previous assessing works focusing mainly on the extraction of aesthetic features in a film, we further study the features, discover their semantic property on videos and then come up with more useful video based features such as motion space and motion direction entropy. In the experiment, we compare the assessing accuracy between two different semantic types of features and find that the semantic-independent feature is more reliable from the results. By combining all features, our method learned a more robust and accurate assessment model.

We propose Binary/Appearance Tracker which consists of background subtraction, silhouette similarity and particle filter to infer pedestrians' locations under different occlusion situations with a single camera. During the period of occlusions, binary and color silhouettes are adaptively used to effectively measure the similarity between the observation and the possible combinations of silhouettes. Thus, the occluded pedestrians' locations can be simply located by the most possible combination of silhouettes. The experimental results show that the proposed BATracker can track people successfully even though she/he is fully occluded.