Good Features to Track
Robust Real-Time Face Detection
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Accelerating pattern matching or how much can you slide?
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking
International Journal of Computer Vision
Intrackability: Characterizing Video Statistics and Pursuing Video Representations
International Journal of Computer Vision
Robust visual tracking using autoregressive hidden Markov Model
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning Global and Reconfigurable Part-Based Models for Object Detection
ICME '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo
| Hi-index | 0.00 |
In this work we present a novel online algorithm to track pedestrian by integrating both the bottom-up and the top-down models of pedestrian. Motivated by the observation that the appearance of a pedestrian changes a lot in different perspectives or poses, the proposed bottom-up model has multiple components to represent distinct groups of the pedestrian appearances. Also, similar pedestrian appearances have several common salient local patterns and their structure is relatively stable. So, each component of the proposed bottom-up model uses an online deformable part-based model (OLDPM) containing one root and several shared parts to represent the flexible structure and salient local patterns of an appearance. We term the bottom-up model multi-component OLDPM in this paper. We borrow an offline trained class specific pedestrian model [19] as the top-down model. The top-down model is used to extend the bottom-up model with a new OLDPM when a new appearance can't be covered by the bottom-up model. The multi-component OLDPM has three advantages compared with other models. First, through an incremental support vector machine (INCSVM) [2] associated with the each component, the OLDPM of each component can effectively adapt to the pedestrian appearance variations of a specified perspective and pose. Second, OLDPM can efficiently generate match penalty maps of parts preserving the 2bit binary pattern (2bitBP) [10] through robust real-time pattern matching algorithm [16], and can search over all possible configurations in an image in linear-time by distance transforms algorithm [5]. Last but not least, parts can be shared among components to reduce the computational complexity for matching. We compare our method with four cutting edge tracking algorithms over seven visual sequences and provide quantitative and qualitative performance comparisons.