Autonomous UAV sensor system for searching and locating VHF radio-tagged wildlife

We consider the problem of tracking and localizing radio-tagged targets, a labor-intensive and time-consuming task necessary for wildlife conservation fieldwork. We design a lightweight sensor system for measurement of radio signal strength information from multiple radio tags. The sensor system is designed to suit low-cost, versatile, easy to operate multi-rotor UAVs. In this demo paper, we demonstrate our Unmanned Aerial Vehicle (UAV) sensor system for tracking and locating multiple VHF radio tags.Hoa Van Nguyen, Michael Chesser, Fei Chen, S. Hamid Rezatofighi, and Damith C. Ranasingh

A Multiple Model Probability Hypothesis density Tracker for Time-lapse Cell Microscopy Sequences

Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters