In this paper, we present a novel approach based on spatio-temporal marked point processes to detect and track moving objects in a batch of high resolution images. Batch processing techniques are applicable to and desirable for a large class of applications such as offline scene and video analysis, and provide better overall detection and data as-sociation accuracy than sequential methods. We develop a new, intuitive energy based model consisting of several terms that take into account both the image evidence and physical constraints such as target dynamics, track persis-tence and mutual exclusion. We construct a suitable op-timization scheme that allows us to find strong local min-ima of the proposed highly non-convex energy. We test our model on three batches of 25 synthetic biological images with different levels of noise. Our main application however consists of two batches of 14 remotely sensed high resolu-tion optical images of boats which are particularly hard to analyze due to the different angles at which the images were taken and the low temporal frequency.