Multi-camera tracking of intelligent targets with Hidden Reciprocal Chains

Real world targets are intelligent and almost always move with a destination in mind. This paper introduces a new target tracking algorithm for multi-camera networks based on a hidden reciprocal chain (HRC), which is able to capture the local dynamics and intention of a real world target in a statistical way. The model is non-causal and therefore fundamentally different to standard Markovian motion models which underpin most trackers, such as the Kalman filter. However it is less computationally expensive than more sophisticated models like Markov decision processes, which can capture complex behaviours but require approximate algorithms for inference. We argue that HRCs are a natural extension to existing Markovian models by presenting exact online inference and detection algorithms which scale well with the number of cameras and targets. Finally we demonstrate the potential benefits by presenting results on synthetic data for the problem of multi-target tracking across multiple cameras.George Stamatescu, Anthony Dick, Langford B. Whit

Decision making with reciprocal chains and binary neural network models

Automated decision making systems are relied on in increasingly diverse and critical settings. Human users expect such systems to improve or augment their own decision making in complex scenarios, in real time, often across distributed networks of devices. This thesis studies binary decision making systems of two forms. The rst system is built from a reciprocal chain, a statistical model able to capture the intentional behaviour of targets moving through a statespace, such as moving towards a destination state. The rst part of the thesis questions the utility of this higher level information in a tracking problem where the system must decide whether a target exists or not. The contributions of this study characterise the bene ts to be expected from reciprocal chains for tracking, using statistical tools and a novel simulation environment that provides relevant numerical experiments. Real world decision making systems often combine statistical models, such as the reciprocal chain, with the second type of system studied in this thesis, a neural network. In the tracking context, a neural network typically forms the object detection system. However, the power consumption and memory usage of state of the art neural networks makes their use on small devices infeasible. This motivates the study of binary neural networks in the second part of the thesis. Such networks use less memory and are e cient to run, compared to standard full precision networks. However, their optimisation is di cult, due to the non-di erentiable functions involved. Several algorithms elect to optimise surrogate networks that are di erentiable and correspond in some way to the original binary network. Unfortunately, the many choices involved in the algorithm design are poorly understood. The second part of the thesis questions the role of parameter initialisation in the optimisation of binary neural networks. Borrowing analytic tools from statistical physics, it is possible to characterise the typical behaviour of a range of algorithms at initialisation precisely, by studying how input signals propagate through these networks on average. This theoretical development also yields practical outcomes, providing scales that limit network depth and suggesting new initialisation methods for binary neural networks.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202