# The Impact of Frame-Dropping on Performance and Energy Consumption for Multi-Object Tracking

arXiv: https://arxiv.org/abs/2304.08152 · HTML: https://mlsanity.com/paper/2304.08152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge](https://mlsanity.com/paper/2609.29648/) (2026)

## Abstract (arXiv)

The safety of automated vehicles (AVs) relies on the representation of their environment. Consequently, state-of-the-art AVs employ potent sensor systems to achieve the best possible environment representation at all times. Although these high-performing systems achieve impressive results, they induce significant requirements for the processing capabilities of an AV's computational hardware components and their energy consumption. To enable a dynamic adaptation of such perception systems based on the situational perception requirements, we introduce a model-agnostic method for the scalable employment of single-frame object detection models using frame-dropping in tracking-by-detection systems. We evaluate our approach on the KITTI 3D Tracking Benchmark, showing that significant energy savings can be achieved at acceptable performance degradation, reaching up to 28% reduction of energy consumption at a performance decline of 6.6% in HOTA score.