{'fraction_non_alphanumeric': 0.029150823827629912, 'fraction_numerical': 0.0038022813688212928, 'mean_word_length': 5.2204724409448815, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards an Efficient Hardware Implementation of CNN-Based Object Trackers

Convolution Neural Network (CNN) has achieved a performance boost to the visual tracking field. However, CNN-based trackers feature slow computational speed and large memory size. These issues challenge the embedded implementation of the CNN-based trackers. In this paper, we show how interpolation schemes can significantly reduce the memory requirements. In addition, we present a design-space exploration of the fixed-point representation of the CNN-based trackers aiming for a cost-efficient hardware implementation. Moreover, we present a hardware accelerator of the online training stage of the tracker. Our proposed accelerator can train the whole fully connected layers at a rate of 45.9 frames-per-second.