Estimating animal abundance is fundamental to ecological research and wildlife management but is often hindered by the high cost, logistical difficulty, and invasiveness of direct observation methods. This Chapter presents the triple Poisson model, a novel hierarchical model for estimating animal abundance from vestige count data, such as scats, under conditions of data scarcity. The model accounts for group size, number of groups, vestige production and decay, and transect coverage, and is flexible enough to incorporate overdispersion through negative binomial extensions. Through extensive simulation studies and comparison with traditional distance sampling methods, we demonstrate that the triple Poisson model yields robust abundance estimates even with limited data, particularly when some prior information is available. We further illustrate its real-world applicability through case studies on collared peccaries, sika deer, and red foxes. This framework provides a cost-effective, non-invasive alternative for estimating wildlife abundance and has potential in ecological monitoring where data are sparse or difficult to collect.