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Active object tracking using context estimation: handling occlusions and detecting missing targets

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Abstract

When performing visual servoing or object tracking tasks, active sensor planning is essential to keep targets in sight or to relocate them when missing. In particular, when dealing with a known target missing from the sensor’s field of view, we propose using prior knowledge related to contextual information to estimate its possible location. To this end, this study proposes a Dynamic Bayesian Network that uses contextual information to effectively search for targets. Monte Carlo particle filtering is employed to approximate the posterior probability of the target’s state, from which uncertainty is defined. We define the robot’s utility function via information theoretic formalism as seeking the optimal action which reduces uncertainty of a task, prompting robot agents to investigate the location where the target most likely might exist. Using a context state model, we design the agent’s high-level decision framework using a Partially-Observable Markov Decision Process. Based on the estimated belief state of the context via sequential observations, the robot’s navigation actions are determined to conduct exploratory and detection tasks. By using this multi-modal context model, our agent can effectively handle basic dynamic events, such as obstruction of targets or their absence from the field of view. We implement and demonstrate these capabilities on a mobile robot in real-time.

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  1. 2617 Wichita St, Austin, TX, 78712, USA

    Minkyu Kim & Luis Sentis

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  1. Minkyu Kim
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  2. Luis Sentis
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Correspondence to Minkyu Kim.

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Appendix: Parameter selection

Appendix: Parameter selection

For particle filtering during experimentation, there are various parameters to be determined, such as the total number of particles, re-sampling conditions (effective sample size) or type of method (residual method), motion model noise, or the sensor model. Here are the parameter list that we empirically determined for our study (Tables 3 and 4).

Table 3 Parameters for Particle Filter
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Table 4 Parameters for POMDP
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Kim, M., Sentis, L. Active object tracking using context estimation: handling occlusions and detecting missing targets. Appl Intell 52, 14041–14052 (2022). https://doi.org/10.1007/s10489-021-03116-5

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