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Enhancing Object Tracking with Caffe: Training and Evaluation of Convolutional Networks

In Week 8, Meera Hahn and Si Chen, mentored by Afshin Deghan, explored advancements in object tracking using Caffe. A 3-layer convolutional network was trained on our video sequences, optimizing parameters like iterations, learning rates, and training images. Additionally, a 5-layer Imagenet network was employed with pre-trained weights, yielding exceptional results. The offline Caffe tracker was tested on 50 sequences, processing greyscale images and generating STRUCK results. We evaluated spatial and temporal robustness, analyzing precision and success rates based on bounding box locations.

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Enhancing Object Tracking with Caffe: Training and Evaluation of Convolutional Networks

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  1. Week 8 Students: Meera Hahn and Si Chen Mentor: AfshinDeghan

  2. Cifar Network • 3 convolutional layer network • Trained Caffe classifier using this network and our training images from the first frames of the video sequence • Experimented with parameters such as number of training iterations, learning rate and number of training images

  3. Imagenet Network • 5 convolutional layers • This is the network we ran to train the offline Caffe tracker with Caffe’s given pre-trained weights which gave very high results • Trained Caffe classifier using this network and our training images from the first frames of the video sequence

  4. F Score Comparisons 45.14 77.01 75.57 62.84 61.24 56.28 • Running Offline Caffe Deep Tracking code on all 50 sequences • Fixed: processing greyscale images • Calculated STRUCK results from benchmark results

  5. Online Object Tracking: A Benchmark 1 Spatial Robustness Evaluation (SRE) Temporal Robustness Evaluation (TRE) Frames evaluated based on bounding box location • 8 spatial shifts of the ground truth = 12 total shifts Precision Plot Success Plot • Error in center location • Overlap in bounding boxes • Ratio of successful tracking 1 Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, “Online Object Tracking: A Benchmark,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

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