X
Xiaokang Jin
Researcher at Changsha University of Science and Technology
Publications - 7
Citations - 418
Xiaokang Jin is an academic researcher from Changsha University of Science and Technology. The author has contributed to research in topics: Convolutional neural network & Video tracking. The author has an hindex of 5, co-authored 5 publications receiving 297 citations.
Papers
More filters
Journal ArticleDOI
A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
TL;DR: This paper proposes an end-to-end convolutional network inspired by YOLOv2 to achieve real-time Chinese traffic sign detection and demonstrates that the proposed method is the faster and more robust.
Journal ArticleDOI
Spatial and semantic convolutional features for robust visual object tracking
TL;DR: A novel model updating strategy is presented, and peak sidelobe ratio (PSR) and skewness are exploited to measure the comprehensive fluctuation of response map for efficient tracking performance.
Journal ArticleDOI
Dual Model Learning Combined With Multiple Feature Selection for Accurate Visual Tracking
TL;DR: This paper proposes dual model learning combined with multiple feature selection for accurate visual tracking and proposes an index named hierarchical peak to sidelobe ratio (HPSR), which determines the activation of an online classifier learning model to redetect the target.
Journal ArticleDOI
A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate
TL;DR: To overcome single feature with poor representation ability in a complex image sequence, a multifeature integration framework, including the gray features, Histogram of Gradient, color-naming, and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking is put forward.
Journal ArticleDOI
Small Sample Face Recognition Algorithm based on Novel Siamese Network
TL;DR: In this paper, a small sample face recognition algorithm based on novel Siamese network is proposed, which does not need rich samples for training and uses pairs of face images as inputs and maps them to target space so that the L2 norm distance in target space can represent the semantic distance in input space.