  Object counting is a challenging task with broad application prospects in
security surveillance, traffic management, and disease diagnosis. Existing
object counting methods face a tri-fold challenge: achieving superior
performance, maintaining high generalizability, and minimizing annotation
costs. We develop a novel training-free class-agnostic object counter,
TFCounter, which is prompt-context-aware via the cascade of the essential
elements in large-scale foundation models. This approach employs an iterative
counting framework with a dual prompt system to recognize a broader spectrum of
objects varying in shape, appearance, and size. Besides, it introduces an
innovative context-aware similarity module incorporating background context to
enhance accuracy within messy scenes. To demonstrate cross-domain
generalizability, we collect a novel counting dataset named BIKE-1000,
including exclusive 1000 images of shared bicycles from Meituan. Extensive
experiments on FSC-147, CARPK, and BIKE-1000 datasets demonstrate that
TFCounter outperforms existing leading training-free methods and exhibits
competitive results compared to trained counterparts.
