Nam Trung Pham

Tracking Multiple Objects using Probability Hypothesis Density Filter and Color Measurements

Most methods for multiple object tracking in video represent the state of multi-object in a high dimensional joint state space. This leads to high computational complexity. This paper presents a method using the probability hypothesis density (PHD) filter to estimate the state of multiple objects in video. The method operates on the single object state space instead of the joint state space. A PHD recursion for visual observations with color measurements is proposed. Our method can track varying number of objects.

Probability hypothesis density approach for multi-camera multi-object tracking

Object tracking with multiple cameras is more efficient than tracking with one camera. In this paper, we propose a multiple-camera multiple-object tracking system that can track 3D object locations even when objects are occluded at cameras. Our system tracks objects and fuses data from multiple cameras by using the probability hypothesis density filter. This method avoids data association between observations and states of objects, and tracks multiple objects in single-object state space. Hence, it has lower computation than methods using joint state space. Moreover, our system can track varying number of objects. The results demonstrate that our method has a high reliability when tracking 3D locations of objects.

Tracking multiple speakers using CPHD filter

In this paper, we present an efficient method for tracking multiple speakers in a reverberant environment. The proposed method is based on the cardinalized probability hypothesis density (CPHD) filter. Because the CPHD filter can handle a large amount of clutter measurements, our method has a high reliability when tracking multiple speakers. Simulation experiments are presented to demonstrate the performance of the proposed method.

Combination of local and global features for near-duplicate detection

This paper presents a new method to combine local and global features for near-duplicate images detection. It mainly consists of three steps. Firstly, the keypoints of images are extracted and preliminarily matched. Secondly, the matched keypoints are voted for estimation of affine transform to reduce false matching keypoints. Finally, to further confirm the matching, the Local Binary Pattern (LBP) and color histograms of areas formed by matched keypoints in two images are compared. This method has the advantage for handling the case when there are only a few matched keypoints. The proposed algorithm has been tested on Columbia dataset and compared quantitatively with the RANdom SAmple Consensus (RANSAC) and the Scale-Rotation Invariant Pattern Entropy (SR-PE) methods. The results turn out that the proposed method compares favorably against the state-of-the-arts.

Learning deep features for multiple object tracking by using a multi-task learning strategy

Model-free object tracking is still challenging because of the limited prior knowledge and the unexpected variation of the target object. In this paper, we propose a feature learning algorithm for model-free multiple object tracking. First, we pre-learn generic features invariant to diverse motion transformations from auxiliary video data by using a deep network of anto-encoder. Then, we adapt the pre-learned features according to multiple target objects respectively in a multi-task learning manner. We treat the feature adaptation for each target object as one single task. We simultaneously learn the common feature shared by all target objects and the individual feature of each object. Experimental results demonstrate that our feature learning algorithm can significantly improve multiple object tracking performance.

Fusing Appearance and Spatio-temporal Features for Multiple Camera Tracking

Multiple camera tracking is a challenging task for many surveillance systems. The objective of multiple camera tracking is to maintain trajectories of objects in the camera network. Due to ambiguities in appearance of objects, it is challenging to re-identify objects when they re-appear in other cameras. Most research works associate objects by using appearance features. In this work, we fuse appearance and spatio-temporal features for person re-identification. Our framework consists of two steps: preprocessing to reduce the number of association candidates and associating objects by using the probabilistic relative distance. We set up an experimental environment including 10 cameras and achieve a better performance than using appearance features only.

Human action recognition via sum-rule fusion of fuzzy K-Nearest Neighbor classifiers

Shape and motion are two most distinct cues observed from human actions. Traditionally, K-Nearest Neighbor (K-NN) classifier is used to compute crisp votes from multiple cues separately. The votes are then combined using linear weighting scheme. Usually, the weights are determined in a brute-force or trial-and-error manner. In this study, we propose a new classification framework based on sum-rule fusion of fuzzy K-NN classifiers. Fuzzy K-NN classifier is capable of producing soft votes, also known as fuzzy membership values. Based on Bayes theorem, we show that the fuzzy membership values produced by the classifiers can be combined using sum-rule. In our experiment, the proposed framework consistently outperforms the conventional counterpart (K-NN with majority voting) for both Weizmann and KTH datasets. The improvement may attribute to the ability of the proposed framework to handle data ambiguity due to similar poses present in different action classes. We also show that the performance of our method compares favorably with the state-of-the-arts.

Combining JPDA and particle filter for visual tracking

Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to maintain tracks of objects. The results of JPDA are employed to improve the observation model in the particle filter. Based on the ability of handling missing detections and clutter of JPDA, tracks of objects can be maintained after merging or splitting. Conversely, the particle filter also improves the performance of JPDA by fusing other observations such as color and background subtraction information. Hence, our method can take advantages from both JPDA and particle filter to track objects through merging and splitting.

Sling bag and backpack detection for human appearance semantic in vision system

In many intelligent surveillance systems there is a requirement to search for people of interest through archived semantic labels. Other than searching through typical appearance attributes such as clothing color and body height, information such as whether a person carries a bag or not is valuable to provide more relevant targeted search. We propose two novel and fast algorithms for sling bag and backpack detection based on the geometrical properties of bags. The advantage of the proposed algorithms is that it does not require shape information from human silhouettes therefore it can work under crowded condition. In addition, the absence of background subtraction makes the algorithms suitable for mobile platforms such as robots. The system was tested with a low resolution surveillance video dataset. Experimental results demonstrate that our method is promising.

Distributed system for multiple camera tracking

In practical surveillance applications, methods for multiple camera tracking have to deal with many challenges such as noises and errors in estimating system parameters. In this paper, we propose a distributed multiple camera tracking system that can track multiple persons in real time under challenging conditions such as inaccuracy from calibration, noises from background and lighting changes. The system combines a network of cameras located around the environment, a central process unit and mobile devices. Experimental results show that our system has already performed well in military training.