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Advancements in CNN Subsampling for Video Tracking: Insights from Initial Experiments

This week's deep tracking experiments by students Si Chen and Meera Hahn, under mentor Afshin Deghan, utilized Caffe to explore CNN subsampling techniques for video analysis. The focus was on integrating pre-initialized weights from supervised pre-training to train a classifier on benchmark datasets using convolutional networks. Results showed 95%+ accuracy using various image sizes and layers. Future steps include testing trained models within tracking code to evaluate performance against pre-trained weights while incorporating motion learning.

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Advancements in CNN Subsampling for Video Tracking: Insights from Initial Experiments

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Presentation Transcript

  1. WEEK 6:DEEP TRACKING Students: Si Chen & Meera Hahn Mentor: AfshinDeghan

  2. Initial Experiments on CNN Subsampling Fully Connected Convolutions Subsampling Convolutions • Using the toolbox by Rasmus Berg Palm • Tracking Framework in complete C2: feature maps 12@10x10 S2: f. maps 12@10x10 S1: feature maps 6@14x14 C1: feature maps 6@28x28

  3. Installation • Overview Caffe • Majority of the week • Code with pre-initialized weights from supervised pre-training • David and Oliver helped us with the installation • Network classifier: 1000 classes --> replaced with an SVM • Last layer: 4096 nodes’ feature activation values --> SVM

  4. F Score Comparisons

  5. CAFFE • Trained weights of the CNN on benchmark data set using: • 256X256 images & 5 convolutional layer network • 32X32 images & 3 convolution layer network • 95%+ accuracy with trained classifier • Expectation: larger images trained with more convolutional layers should produce better results • Next step: Put trained models into tracker

  6. Next steps • Trained model into our tracker code: • How well does the tracker preform in comparison to using pre-trained weights? • Fully connected network • Learning additional attributes of videos: • Motion: provide temporal data to the network so it can learn the motion • Scale change

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