ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles

ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles

Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of powerful client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices to achieve low latency and real-time processing and to preserve user privacy.However, most video analytic systems are heavyweight and are trained offline with some pre-defined latency or accuracy requirements. This makes them unable to adapt at runtime in the face of three types of dynamism -- the input video characteristics change, the amount of compute resources available on the node changes due to co-located applications, and the user's latency-accuracy requirements change. In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios. To achieve this, we introduce a multi-branch object detection kernel (layered on Faster R-CNN), which incorporates a data-driven modeling approach on the performance metrics, and a latency SLA-driven scheduler to pick the best execution branch at runtime. We couple this kernel with approximable video object tracking algorithms to create an end-to-end video object detection system. We evaluate ApproxDet on a large benchmark video dataset and compare quantitatively to AdaScale and YOLOv3. We find that ApproxDet is able to adapt to a wide variety of contention and content characteristics and outshines all baselines, e.g., it achieves 52% lower latency and 11.1% higher accuracy over YOLOv3.

ApproxDet：用于移动设备的内容和竞争感知近似对象检测

包括场景分类和对象检测在内的高级视频分析系统已在智能城市和自动交通等各个领域获得了广泛的成功。随着功能强大的客户端设备数量的不断增长，人们有动力将这些繁重的视频分析工作负载从云转移到移动设备，以实现低延迟和实时处理并保护用户隐私。.. 但是，大多数视频分析系统都是重量级的，并根据一些预定义的延迟或准确性要求进行脱机培训。这使它们无法在运行时适应三种类型的动态-输入视频特征更改，节点上可用的计算资源量由于共处一地的应用程序而更改，以及用户的延迟准确性要求更改。在本文中，我们介绍了ApproxDet，这是一种适用于移动设备的自适应视频对象检测框架，可以在内容和资源争用场景不断变化的情况下满足准确性-延迟要求。为此，我们引入了一个多分支对象检测内核（位于Faster R-CNN上），该内核在性能指标上采用了数据驱动的建模方法，延迟SLA驱动的调度程序可以在运行时选择最佳执行分支。我们将此内核与近似视频对象跟踪算法结合在一起，以创建端到端视频对象检测系统。我们在大型基准视频数据集上评估ApproxDet，并与AdaScale和YOLOv3进行定量比较。我们发现，ApproxDet能够适应各种争用和内容特征，并且超越所有基准，例如，与YOLOv3相比，它的延迟降低了52％，准确性提高了11.1％。 （阅读更多）