We are interested in establishing the correspondence between neuron activity and body curvature during various
movements of C. Elegans worms. Given long sequences of images, specifically recorded to glow when the neuron
is active, it is required to track all identifiable neurons in each frame. The characteristics of the neuron data,
e.g., the uninformative nature of neuron appearance and the sequential ordering of neurons, renders standard
single and multi-object tracking methods either ineffective or unnecessary for our task. In this paper, we propose
a multi-target tracking algorithm that correctly assigns each neuron to one of several candidate locations in the
next frame preserving shape constraint. The results demonstrate how the proposed method can robustly track
more neurons than several existing methods in long sequences of images.