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This study presents MaxBoxCount, the winning solution to the iWildCam 2021 Challenge, which addresses the challenge of counting unique animals in camera trap image sequences without relying on count annotations. By integrating a robust species classification pipeline with a counting heuristic derived from MegaDetector detections, the method effectively estimates individual counts despite the temporal discontinuities inherent in camera trap data. The results demonstrate that MaxBoxCount can accurately estimate animal populations, significantly advancing automated wildlife monitoring techniques.
Counting unique animals in camera trap sequences is now possible without any count labels, thanks to a novel heuristic that leverages existing detection models.
Camera traps have become an essential tool for wildlife monitoring, motivating the development of computer vision methods for the automated extraction of information from these data. While most prior work has focused on species identification, many ecological applications also require estimating the number of unique individuals appearing across short image sequences. This task is particularly challenging because camera traps typically acquire bursts of images at approximately one frame per second, creating large temporal discontinuities that may make conventional multi-object tracking methods unreliable, and because manually collecting individual count annotations is prohibitively expensive. In this work, we describe the winning solution to the iWildCam 2021 Challenge, which introduced a benchmark for counting animals at the sequence level under realistic annotation constraints where count annotations are unavailable for training. Our approach, MaxBoxCount, combines a strong species classification pipeline with a simple yet effective counting heuristic based on MegaDetector detections to estimate the number of unique individuals without requiring count annotations. Code is available at https://github.com/alcunha/iwildcam2021ufam.
