A new approach for multi-target tracking in an occlusion environment is presented. In pedestrian tracking using a video camera, pedestrains must be tracked accurately and continuously in the images. However, in a crowded environment, the conventional tracking algorithm has a problem in that tracks do not continue when pedestrians are hidden behind the foreground object. In this study, we propose a robust tracking method for occlusion that introduces a degeneration hypothesis that relaxes the track hypothesis which has one measurement to one track constraint. The proposed method relaxes the hypothesis that one measurement and multiple trajectories are associated based on the endpoints of the bounding box when the predicted trajectory is approaching, therefore the continuation of the tracking is improved using the measurement in the foreground. A numerical evaluation using MOT (Multiple Object Tracking) image data sets is performed to demonstrate the effectiveness of the proposed algorithm.

We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.

In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. It can first obtain the target center position of next frame by computing the maximum of the response, where the position correlation filter is learned by the least squares classifier and the dimensionality reduction for color features is analyzed by principal component analysis. The scale correlation filter is obtained by color characteristics at 33 rectangular areas which is set by the scale factor around the central location and is reduced dimensions by orthogonal triangle decomposition. Finally, the location and size of the target are updated by the maximum of the response. By testing 13 challenging video sequences taken by the drone, the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination Variation, fast motion, motion blur and other complex situations.

The correlation filter-based trackers with an appearance model established by single feature have poor robustness to challenging video environment which includes factors such as occlusion, fast motion and out-of-view. In this paper, a long-term tracking algorithm based on multi-feature adaptive fusion for video target is presented. We design a robust appearance model by fusing powerful features including histogram of gradient, local binary pattern and color-naming at response map level to conquer the interference in the video. In addition, a random fern classifier is trained as re-detector to detect target when tracking failure occurs, so that long-term tracking is implemented. We evaluate our algorithm on large-scale benchmark datasets and the results show that the proposed algorithm have more accurate and more robust performance in complex video environment.

Particle filter algorithm is an important algorithm in the field of target tracking. however, this algorithm faces the problem of sample impoverishment which is caused by the introduction of re-sampling and easily affected by illumination variation. This problem seriously affects the tracking performance of a particle filter algorithm. To solve this problem, we introduce a particle filter target tracking algorithm based on a dynamic niche genetic algorithm. The application of this dynamic niche genetic algorithm to re-sampling ensures particle diversity and dynamically fuses the color and profile features of the target in order to increase the algorithm accuracy under the illumination variation. According to the test results, the proposed algorithm accurately tracks the target, significantly increases the number of particles, enhances the particle diversity, and exhibits better robustness and better accuracy.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.

In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.

For an indoor area where a target node is tracked with anchor nodes, we can calculate the priori probability density functions (pdfs) on the distances between the target and anchor nodes by using its shape, three-dimensional sizes and anchor nodes locations. We call it “the area layout information (ALI)” and apply it for two indoor target tracking methods with received signal strength indication (RSSI) assuming a square location estimation area. First, we introduce the ALI to a target tracking method which tracks a target using the weighted sum of its past-to-present locations by a simple infinite impulse response (IIR) low pass filter. Second, we show that the ALI is applicable to a target tracking method with a particle filter where the motion of the target is nonlinearly modelled. The performances of the two tracking methods are evaluated by not only computer simulations but also experiments. The results demonstrate that the use of ALI can successfully improve the location estimation performance of both target tracking methods, without huge increase of computational complexity.

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.

Target tracking is one of the key applications of Wireless Sensor Networks (WSNs) that forms basis for numerous other applications. The overall procedures of target tracking involve target detection, localization, and tracking. Because of the WSNs' resource constraints (especially energy), it is highly desired that target tracking should be done by involving as less number of sensor nodes as possible. Due to the uncertain behavior of the target and resulting mobility patterns, this goal becomes harder to achieve without predicting the future locations of the target. The presence of a prediction mechanism may allow the activation of only the relevant sensors along the future course, before actually the target reaches the future location. This prior activation contributes to increasing the overall sensor networks lifetime by letting non-relevant nodes sleep. In this paper, first, we introduce a Yaw rate aware sensor wAkeup Protocol (YAP) for the prediction of future target locations. Second, we present improvements on the YAP design through the incorporation of adaptability. The proposed schemes are distributive in nature, and select relevant sensors to determine the target track. The performance of YAP and A-YAP is also discussed on different mobility patterns, which confirms the efficacy of the algorithm.

This paper deals with the problem of multiple object tracking with the condensation algorithm, applied to tracking of soccer players. To solve the problem of failures in tracking multiple players under overlapping, we introduce occlusion alarm probability, which attracts or repels particles based on their posterior distribution of previous time step. Real experiments showed a robust performance.

For radar tracking, the α-β filter and the Kalman filter, both of which do not require large computational requirements, have been widely utilized. However these filters cannot track a maneuvering target accurately. In recent years, the IMM (Interactive Multiple Model) algorithm has been proposed. The IMM is expected to reduce tracking errors for both non-maneuvering and maneuvering target. However, the IMM requires heavy computational burden, because it utilizes multiple Kalman filters in parallel. On the other hand, the α-β filter with an acceleration term which can estimate maneuver acceleration from the past target estimated positions using the kinetic model, has been proposed. This filter is not available for tracking targets under clutter environment, since it does not calculate the covariance matrix which is needed for gate setting. In this paper, we apply the acceleration estimate to the Kalman filter, and propose the hybrid Kalman filter with a constant-velocity filter and an acceleration estimation filter, and it integrates the outputs of two filters using the normalized distance of the prediction error of each filter. The computational requirement of the proposed filter is smaller than that of the IMM since the proposed filter consists of only two Kalman based filters. The proposed method can prevent deteriorating tracking accuracy by reducing the risk of maneuver misdetection when a target maneuvers. We evaluate the performance of the proposed filter by computer simulation, and show the effectiveness of the proposed filter, comparing with the conventional Kalman filter and the two-stage Kalman filter.

An analytic approximation of the information reduction factor is presented in an efficient manner for the case of two-dimensional measurement vector and a four-sigma validation gate. This analytic approximation allows us to efficiently evaluate performance prediction for the probabilistic data association (PDA) filter using the hybrid conditional average (HYCA) algorithm.

This paper proposes a maneuvering target tracking algorithm using multiple model filters. This filtering algorithm is discussed in terms of tracking performance, tracking success rate and tracking accuracies for short sampling interval as compared with other conventional methodology. Through several simulations, validity of this algorithm has been confirmed.

This paper presents a new multi-target data association method for automotive radar which we call the order statistics joint probabilistic data association (OSJPDA). The method is formulated using the association probabilities of the joint probabilistic data association (JPDA) filter and an optimal target-to-measurement data association is accomplished using the decision logic algorithm. Simulation results for heavily cluttered conditions show that the tracking performance of the OSJPDA filter is better than that of the JPDA filter in terms of tracking accuracy by about 18%.

We propose a new target motion modeling method using target-oriented coordinates by decomposing the acceleration into the target's moving direction component and its orthogonal component. We apply this physically more rational target-oriented model assumption to representative IMMPDAF scenarios and show that the tracking error is reduced by up to 14%.

An averaged intensity peak profile of light scattered from a random medium depends on a thickness of a sample as well as parameters such as a volume fraction and a size of particles composing the medium. We used this dependence to measure a depth profile varied in the random medium. We demonstrated the possible simultaneous measurement of a transport mean free path and a depth of an aqueous suspension of titanium particles.

Tracking many targets simultaneously using a search radar has been one of the major research areas in radar signal processing. The primary difficulty in this problem arises from the noise characteristics of the incoming data. Hence it is crucial to obtain an accurate association between targets and noisy measurements in multi-target tracking. We introduce a new scheme for optimal data association, based on a MAP approach, and thereby derive an efficient energy function. Unlike the previous approaches, the new constraints between targets and measurements can manage the cases of target missing and false alarm. Presently, most algorithms need heuristic adjustments of the parameters. Instead, this paper suggests a mechanism that determines the parameters in an automated manner. Experimental results, including PDA and NNF, show that the proposed method reduces position errors in crossing trajectories by 32.8% on the average compared to NNF.