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.


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    Title :

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


    Contributors:


    Publication date :

    2023-06-04


    Size :

    1845022 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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