PhDDistributed target tracking (DTT) is desirable in wireless camera networks to achieve scalability
and robustness to node or link failures. DTT estimates the target state via information
exchange and fusion among cameras. This thesis proposes new DTT algorithms to handle five
major challenges of DTT in wireless camera networks, namely non-linearity in the camera measurement
model, temporary lack of measurements (benightedness) due to limited field of view,
redundant information in the network, limited connectivity of the network due to limited communication
ranges and asynchronous information caused by varying and unknown frame processing
delays. The algorithms consist of two phases, namely estimation and fusion. In the estimation
phase, the cameras process their captured frames, detect the target, and estimate the target
state (location and velocity) and its uncertainty using the Extended Information Filter (EIF) that
handles non-linearity. In the fusion phase, the cameras exchange their local target information
with their communicative neighbours and fuse the information. The contributions of this thesis
are as follows. The target states estimated by the EIFs undergo weighted fusion. The weights
are chosen based on the estimated uncertainty (error covariance) and the number of nodes with
redundant information such that the information of benighted nodes and the redundant information
get lower weights. At each time step, only the cameras having the view of the target
and the cameras that might have the view of the target in the next time step participate in the
fusion (tracking). This reduces the energy consumption of the network. The algorithm selects
the cameras dynamically by using a threshold on their shortest distances (in the communication
graph) from the cameras having the view of the target. Before fusion, each camera predicts the
target information of other cameras to temporally align its information with the (asynchronous)
information received from other cameras. The algorithm predicts the target state using the latest
estimated velocity of the target. The experimental results show that the proposed algorithms
achieve higher tracking accuracy than the state of the art under the five DTT challenges
