Implement tracking algorithm using CNNs

Convolutional neural networks (CNNs) is widely used as classifiers in the field of computer vision. The more complex a CNNs model is, the more accurate classification results will be. But a very deep network also requires a better GPU to train and test in a reasonable time. In this paper, we purpose...

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Bibliographic Details
Main Authors: Li, Chaoran, Xi, Yuling, Ding, Songtao
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Classification
Computational modeling
Computer vision
Conferences
Convolutional Neural Networks
Feature extraction
Graphics processing units
Kernels
Neural networks
Object Tracking
Target tracking
Tracking
Visualization
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Summary:Convolutional neural networks (CNNs) is widely used as classifiers in the field of computer vision. The more complex a CNNs model is, the more accurate classification results will be. But a very deep network also requires a better GPU to train and test in a reasonable time. In this paper, we purpose a tracking algorithm using LeNet-5, and avoid computing complex handcrafted features from the raw inputs. Modifying the number of kernels improves the tracking accuracy of models, avoid over-fitting. The experiment of processing a video clip meets real-time requirement while using GPU and it shows our algorithm is more robust than traditional algorithm like particle filter to track single target under the complicated background.
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2016.7554485