On The Stability of Video Detection and Tracking

Abstract

In this paper, we study an important yet less explored aspect in video detection and tracking -- stability. Surprisingly, there is no prior work that tried to study it. As a result, we start our work by proposing a novel evaluation metric for video detection which considers both stability and accuracy. For accuracy, we extend the existing accuracy metric mean Average Precision (mAP). For stability, we decompose it into three terms: fragment error, center position error, scale and ratio error. Each error represents one aspect of stability. Furthermore, we demonstrate that the stability metric has low correlation with accuracy metric. Thus, it indeed captures a different perspective of quality. Lastly, based on this metric, we evaluate several existing methods for video detection and show how they affect accuracy and stability. We believe our work can provide guidance and solid baselines for future researches in the related areas.

References in corpus (5)

• MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
• T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
• Seq-NMS for Video Object Detection
• HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
• STFCN: Spatio-Temporal FCN for Semantic Video Segmentation

Cited by in corpus (5)

• Flow-Guided Feature Aggregation for Video Object Detection
• Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
• An Analysis of Deep Object Detectors For Diver Detection
• Multiple Object Tracking with Correlation Learning
• Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors