Probabilistic Model of Object Detection Based on Convolutional Neural Network
This work addresses the issue of wasted regional information and the trade-off between efficiency and accuracy in object detection for computer vision applications, representing an incremental improvement.
The paper tackles the problem of inefficient and inaccurate object detection by proposing a probabilistic model combined with a search framework, which maps images into probabilistic distributions of objects to provide more informative outputs with less computation, achieving sound, efficient, and analytic results on the FDDB dataset.
The combination of a CNN detector and a search framework forms the basis for local object/pattern detection. To handle the waste of regional information and the defective compromise between efficiency and accuracy, this paper proposes a probabilistic model with a powerful search framework. By mapping an image into a probabilistic distribution of objects, this new model gives more informative outputs with less computation. The setting and analytic traits are elaborated in this paper, followed by a series of experiments carried out on FDDB, which show that the proposed model is sound, efficient and analytic.