CONVOULTION NEURAL NETWORK IN VISUAL TRACKING USING CORRELATION FILTER

@article{1701120,
      author = {V. Iswarya, Mrs. D. Sindhu},
      title = {CONVOULTION NEURAL NETWORK IN VISUAL TRACKING USING CORRELATION FILTER},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {10},
      pages = {45-50},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701120.pdf},
      abstract = {In the past years correlation filter have shown impressive results for visual object tracking.The types of features present in this tracker affect the performance of visual object tracking.The goal is to utilize object detection features Whenever change the appearance of the object.In this project correlation filter is invoked in convolution neural network and find a location of object. Correlation filter based (CFB) trackers it used the network for classification problem. Based on the loss function of network a back propagation algorithm is used for the proposed model.The newly proposed model is flexible with custom design and also makes dependency on network trained for classification. Convolution part of state of art network must be fine-tuned and get achieved in performance by 20%.Tracking failures must be decreased by 30% when we use the tracking dataset VOT-2016(visual object tracking)},
      keywords = {visual tracking, correlation filter, correlation plane, Loss function},
      month = {April},
  }