Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering

Abstract

In this paper, we propose the methods to handle temporal errors during multi-object tracking. Temporal error occurs when objects are occluded or noisy detections appear near the object. In those situations, tracking may fail and various errors like drift or ID-switching occur. It is hard to overcome temporal errors only by using motion and shape information. So, we propose the historical appearance matching method and joint-input siamese network which was trained by 2-step process. It can prevent tracking failures although objects are temporally occluded or last matching information is unreliable. We also provide useful technique to remove noisy detections effectively according to scene condition. Tracking performance, especially identity consistency, is highly improved by attaching our methods.

Aerial Multi-Object Tracking by Detection Using Deep Association Networks

“…They fuse the head and full body detectors for tracking purposes. In [50], authors provide mechanisms o handle temporal errors in tracking such as drifting and track ID switches. This happens due to occlusion or noise present in the scene.…”

Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

“…License plate recognition (LPR) is a fundamental and essential process of identifying vehicles and can be extended to a variety of real-world applications. LPR methods have been widely studied over the last decade, and are especially of big interest in intelligent transport systems (ITS) applications such as access control (Chinomi et al, 2008), road traffic monitoring (Noh et al, 2016;Pu et al, 2013;Song and Jeon, 2016;Lee et al, 2017;Yoon et al, 2018) and traffic law enforcement (Zhang et al, 2011). Since all license plate recognition methods always deal with the letters and numbers in images, they are closely related to image classification (Simonyan and Zisserman, 2014;Russakovsky et al, 2015) and text localization (Anagnostopoulos et al, 2006).…”

Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

“…On the other hand, since the online approach cannot apply the global optimization models, intensive motion analysis and appearance feature learning have been popularly utilized with a hierarchical data association framework and the online Bayesian model [14], [15], [18], [21], [23], [25], [37], [42]. Yoon et al [23] proposed a relative motion analysis between all objects in a frame, and then improved the work [23] by adding the cost optimization function using context constraints in [21].…”

“…So, some online MOT algorithms have focused on how to adopt deep appearance learning into their tracking frameworks. Yoon et al [15] exploited the siamese convolutional neural networks (CNN) [51] to train appearance model. They train the deep appearance networks selectively where only the detection responses matched with high confidence between the historical object queues in the recent few frames.…”

“…Then, they associate the pairs of detections and tracks which make the sum of the distances minimal. Both approaches [15], [37] proposed online Bayesian tracking models with conventional DNN models to measure the similarity between the visual objects. Those online MOT methods have proposed successful solutions with excellent tracking accuracy but their intensive analysis and learning processes take heavy computing resource and time.…”