DeepAI AI Chat
Log In Sign Up

On The Stability of Video Detection and Tracking

11/20/2016
by   Hong Zhang, et al.
0

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.

READ FULL TEXT
12/22/2019

Continuity, Stability, and Integration: Novel Tracking-Based Perspectives for Temporal Object Detection

Video object detection (VID) has been vigorously studied for years but a...
11/26/2022

On the Stability and Accuracy of Clenshaw-Curtis Collocation

We study the A-stability and accuracy characteristics of Clenshaw-Curtis...
07/12/2018

Robustness Analysis of Pedestrian Detectors for Surveillance

To obtain effective pedestrian detection results in surveillance video, ...
11/27/2018

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

Accurate detection and tracking of objects is vital for effective video ...
10/05/2017

Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

The aspect ratio variation frequently appears in visual tracking and has...
07/30/2022

Learning Shadow Correspondence for Video Shadow Detection

Video shadow detection aims to generate consistent shadow predictions am...