Mk Bashar

Multiple Object Tracking (MOT) is a core capability in modern computer vision, essential to autonomous driving, surveillance, sports analytics, robotics, and biomedical imaging. Persistent identity assignment across frames remains challenging in real scenes because of occlusion, dense crowds, appearance ambiguity, scale variation, camera motion, and identity switching. In this survey we synthesize recent progress by organizing methods around the problems they target and the paradigms they adopt. We cover the historical progression from tracking-by-detection to hybrid and end-to-end designs, and we summarize major architectural directions including transformer-based trackers, generative/diffusion formulations, state-space predictors, Siamese and graph-based models, and the growing impact of foundation models for detection and representation. We review benchmark trends that motivate method design, documenting the shift from saturated pedestrian benchmarks to challenge-driven and domain-specific datasets and we analyze evaluation practice by comparing classic and newer motion- and safety-centric metrics. Finally, we connect algorithmic trends to practical deployment constraints and outline emerging directions, foundation-model integration, open-vocabulary and multimodal tracking, unified evaluation, and domain-adaptive methods, that we believe will shape MOT research and real-world adoption.

Sow body condition is an important indicator for growers because it affects lactation performance and piglet survival, but common production measures such as visual scoring and calipers correlate poorly with underlying tissue composition. PigFormer is an end-to-end two-stage system that takes raw depth frames from a ceiling-mounted RGB-D camera and predicts subcutaneous backfat thickness, loin muscle depth, and total tissue thickness at the last rib. A geometric front-end standardizes depth into height maps, and a Slice Attention Encoder models cross-sectional slices along the dorsal surface. On a multi-site dataset of 319 sow and gilt instances (6,705 frames) from two facilities, PigFormer achieves 2.43 mm backfat MAE and 3.91 mm overall MAE.

We present a camera-based method for estimating the respiratory rate of naturally sleeping pigs from video. Our pipeline leverages dense point tracking with CoTracker to capture subtle belly motion, followed by Butterworth bandpass filtering and correlation-based trajectory grouping to isolate breathing signals. The trajectories are analyzed using Fast Fourier Transform (FFT) to extract the dominant respiratory frequency. We evaluate the method on a dataset of 55 nighttime video samples captured of group-housed pigs, achieving a mean absolute error of 1.48 breaths per minute. Compared to an intensity-based baseline, our approach shows significantly higher accuracy and robustness under low-light, cluttered farm conditions. Ablation studies further demonstrate the contribution of each component and the impact of query region size. This work offers a non-invasive solution for automated respiratory monitoring in livestock environments.