LIDAR-BASED MULTI-OBJECT TRACKING SYSTEM WITH DYNAMIC MODELING: 2012, 석사 학위 논문
복합한 환경에서의 다중 물체 추적시 발생 가능한 association의 불확실성을 제거 하기 위해서는 JPDA가 모든 가능한 연관 확률 값을 고려하므로 유용하다. To eliminate association ambiguity in complex scenes, especially for multi-objective tracking, JPDA is a data association algorithm that takes into account every possible association.
이 방식은 센서로 탐지된 위치와 기존 Track간의 가능성을 Bayesian estimate 을 계산하여 hypothesis matrix 형태로 저장 한다. 이후 가장 높은 확률값을 가지고 있는것을 할당한다. It computes the Bayesian estimate of the correspondence between segments detected by sensor and possible existing tracks and forms a hypothesis matrix including all possible associations. The assignments with highest probability are picked out.
[45, 62]에서는 최초로 JPDA를 적용하여 가능성을 보였다. As an example of JPDA, Schulz applied sample-based JPDA in laser-based tracking system at first and showed its effectiveness for the multiple people tracking problem (Figure 2.16) [45, 62].
[46]에서는 JPDA를 수정한 방식을 제안 하였다. By modifying JPDA to separate highly-correlated target-path combinations from poorly-correlated combinations, Frank, et al. proposed two extended JPDA approaches and tested them off-line (Figure 2.17) [46].
DARPA 2007 에서 JPDA가 이용되었다. The robot in DARPA 2007 challenge, Junior, mentioned earlier in Section 2.2.2.2, also used this JPDA approach.
Joint Probability Data Association Filter by Tobias Marsena : youtube, 초 간단한 설명