Week 8

Week 8 Students: Meera Hahn and Si Chen Mentor: Afshin Deghan

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

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

F Score Comparisons • Running Offline Caffe Deep Tracking code on all 50 sequences • Fixed: processing greyscale images • Calculated STRUCK results from benchmark results

Autoencoder Fully Connected Offline CAFFE STRUCK + SVM Network Deep Tracker 115 Full

62.84

45.14

77.01

75.57

61.24

56.28

Online Object Tracking: A Benchmark 1 Spatial Robustness Evaluation (SRE) • 8 spatial shifts of the ground truth = 12 total shifts

Precision Plot • Error in center location

Temporal Robustness Evaluation (TRE) • Frames evaluated based on bounding box location

Success Plot • Overlap in bounding boxes

• Ratio of successful tracking Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang, “Online Object Tracking: A Benchmark,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. 1