具遮蔽處理之貝氏濾波法實現主動相機平台之多目標物影像追蹤

Abstract

在影像追蹤的應用當中，多目標物追蹤系統(MTT System)常會遇到一個問題，即移動的目標物無可避免地會在影像當中彼此交錯，因此對個別目標物作外觀的量測過程中便產生了目標物間的相依性。在本論文當中，我們提出了使用結合影像相似度(Joint Image Likelihood)以及對目標物距離相機的相對深度層次(Depth Level)的假設來分辨交錯中的目標物，實驗證明即使目標物的外觀相似仍可維持目標追蹤。為了同時達到多目標物的偵測及追蹤，我們將SIR粒子濾波器(Particle Filter)延伸為分散式SIR粒子濾波器；然而為了增加系統在處理交錯目標物的分辨時的效能，我們採用以馬可夫鏈蒙地卡羅(Markov Chain Monte Carlo)為基礎之粒子濾波器，此方法可以有效率地解決目標物交錯時的高維度狀態估測。本論文另一個重點在於提出主動相機的控制策略，使該系統可自動移動相機視角使得監視範圍中能包含最多的資訊量。最後，透過實驗來驗證此即時系統整體的效能及可靠性。

In visual tracking, multi-target tracking (MTT) systems encounter the problem that unavoidably moving targets may occlude each other and the measurement process of each target becomes dependent. We construct a tracking system with considering joint image likelihood to recognize targets, even though the appearances of the target are identical. Also, the multiple hypotheses of the targets’ depth level are utilized for occlusion handling. In order to enhance system performance, we extend the sampling importance resampling (SIR) particle filter with the separated importance functions for tracking each target and detection. Furthermore, when targets occlude together, the state vector of these targets is transferred into a joint state vector, and the MCMC (Markov Chain Monte Carlo) based particle filter is then proposed for efficient sampling in the high-dimensional joint state during occlusion. Furthermore, a control strategy for the active camera is proposed in order to move the camera such that the surveillance area will contain the most information. The overall performance is validated in the experiments and shows the robustness with real-time tracking.