A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification

Abstract

Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.