Class agnostic moving target detection by color and location prediction of moving area

Abstract

Moving target detection plays an important role in computer vision. However, traditional algorithms such as frame difference and optical flow usually suffer from low accuracy or heavy computation. Recent algorithms such as deep learning-based convolutional neural networks have achieved high accuracy and real-time performance, but they usually need to know the classes of targets in advance, which limits the practical applications. Therefore, we proposed a model free moving target detection algorithm. This algorithm extracts the moving area through the difference of image features. Then, the color and location probability map of the moving area will be calculated through maximum a posteriori probability. And the target probability map can be obtained through the dot multiply between the two maps. Finally, the optimal moving target area can be solved by stochastic gradient descent on the target probability map. Results show that the proposed algorithm achieves the highest accuracy compared with state-of-the-art algorithms, without needing to know the classes of targets. Furthermore, as the existing datasets are not suitable for moving target detection, we proposed a method for producing evaluation dataset. Besides, we also proved the proposed algorithm can be used to assist target tracking.

References in corpus (8)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• High-Speed Tracking with Kernelized Correlation Filters
• Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
• Salient Object Detection: A Benchmark
• Video Salient Object Detection via Fully Convolutional Networks
• T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
• Object Detection from Video Tubelets with Convolutional Neural Networks
• Video Salient Object Detection Using Spatiotemporal Deep Features