A Survey of Motion-Based Multitarget Tracking Methods

Abstract

Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

“…Other techniques are also available such as the Multiple Hypothesis Tracking (MHT) mentioned in Section I [12]. MHT is known to be more efficient than JPDAF but at the cost of an unrealistically increased computation time for the considered application if the tracking should happen in real time [24]. Similar reasoning was applied for the choice of the UKF as tracking filter compared to other methods such as particle filters that are more efficient but more computationally intensive [20].…”

A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots

“…The most interesting aspect of the position tracker is the computation of association probabilities. In other multi-target tracking systems, the data association is usually done based on euclidean or Mahalanobis distance of the new detection and the existing hypotheses [33]. In our approach, we adapted this in order to realize the following requirements: First, the spatially closest hypothesis should have the highest probability to be assigned to a new detection, but we wanted to define a distance r max from which it is unlikely that the hypothesis belongs to the detection (a h,d <= 0.5).…”

Survey: mobile sensor networks for target searching and tracking

“…Rather, it is to give a brief introduction through some surveys dealing with the problem of target tracking. The remainder of this section is based on the following surveys: Blackman [64], Pulford [65], Mallick et al [66], and Qiu et al [67].…”