HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery

Abstract

Wide Area Motion Imagery (WAMI) yields high resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net). HM-Net is significantly faster than state-of-the-art frame differencing and background subtractionbased methods, without compromising detection and tracking performances. HM-Net follows object centerbased joint detection and tracking paradigm. Simple heat map-based predictions support unlimited number of simultaneous detections. The proposed method uses two consecutive frames and the object detection heat map obtained from the previous frame as input, which helps HM-Net monitor spatio-temporal changes between frames and keep track of previously predicted objects. Although reuse of prior object detection heat map acts as a vital feedback-based memory element, it can lead to unintended surge of false positive detections. To increase robustness of the method against false positives and to eliminate low confidence detections, HM-Net employs novel feedback filters and advanced data augmentations. HM-Net outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset with its 96.2% F1 and 94.4% mAP detection scores, while achieving 61.8% mAP tracking score on the same dataset.This performance corresponds to an improvement of 2.1% for F1, 6.1% for mAP scores on detection, and 9.5% for mAP score on tracking over state-of-the-art.

Usage of Vehicle Re-Identification Models for Improved Persistent Multiple Object Tracking in Wide Area Motion Imagery

“…In [15], the authors adopt a Gaussian Mixture Probabilistic Hypothesis filter to associate detections obtained by a regression CNN. Motorcu et al [16] adopt a regression network for object center detection and tracking in WAMI.…”

Enhanced GM-PHD Filter for Real Time Satellite Multi-Target Tracking

“…Recent works address object detection with deep learningbased techniques. Anchor-free methods [7,8,9,10] obtain remarkable scores on the WPAFB benchmark [11]. However, when extended to object tracking, purely deep learning methods perform frame-to-frame-based tracking and fail to recover trajectory uncertainties.…”