Conv W Conv conv W Position in Estimated Cropped search Window Tracking Output Figure 3 Main steps of the proposed algorithm. Given an image, we first crop the search window centered at the estimated position in the previous frame. We use the third, fourth and fifth convolu- tional layers as our target representations. Each layer indexed by i is then convolved with the learned linear correlation filter w to generate a response map, whose location of the maximum value indicates the estimated target position. We search the multi-level response maps to infer the target location in a coarse-to-fine fash- ion
Algo orithm Use the convolutional feature maps from a cnn alex Net or VGG-Net to encode target appearance along with the cnn forward propagation, the semantical discrimination between objects from different categories is strengthened, as well as a gradual reduction of spatial resolution for precise localization Learn a discriminative classifier and estimate the translation of target objects by searching for the maximum value of correlation response map. Given the set of correlation response maps we hierarchically infer the target translation of each layer
Algorithm • Use the convolutional feature maps from a CNN, AlexNet or VGG-Net to encode target appearance. Along with the CNN forward propagation, the semantical discrimination between objects from different categories is strengthened, as well as a gradual reduction of spatial resolution for precise localization. • Learn a discriminative classifier and estimate the translation of target objects by searching for the maximum value of correlation response map. • Given the set of correlation response maps , we hierarchically infer the target translation of each layer
Implementation details Experimental Validations
•Implementation Details • Experimental Validations
Conclusion Combine cnn and correlation filters together. Use not only the last layer but also the early layers of cnn to achieve better performance Extensive experimental results show that the proposed algorithm performs favorably against the state-of-the -art methods in terms of accuracy and robustness
Conclusion • Combine CNN and Correlation Filters together. • Use not only the last layer but also the early layers of CNN to achieve better performance. • Extensive experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of • accuracy and robustness
Online obiect tracking with Proposal selection Reporter: Liu Cun Student i:115413910018 201605.03
Online Object Tracking with Proposal Selection Reporter : Liu Cun Student ID: 115413910018 2016.05.03