arXiv subjects

Zheng Tang

Publications and source records attributed to Zheng Tang.

41 records · Page 3Linked to original sources

arXiv2019

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.

cs.CV↗

arXiv2019

MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D

Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models built on individual or several selected frames for the comparison of features, but they cannot encode long-term appearance changes caused by pose, viewing angle and lighting conditions. In this work, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by adaptive update and spatial weighting schemes. To handle occlusion and nearby objects sharing similar appearance, we also design cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. Additionally, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state-of-the-art on the MOTChallenge 3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state-of-the-art on the 2D benchmark of MOTChallenge.

cs.CV↗

arXiv2017

Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration

Tracking of multiple objects is an important application in AI City geared towards solving salient problems related to safety and congestion in an urban environment. Frequent occlusion in traffic surveillance has been a major problem in this research field. In this challenge, we propose a model-based vehicle localization method, which builds a kernel at each patch of the 3D deformable vehicle model and associates them with constraints in 3D space. The proposed method utilizes shape fitness evaluation besides color information to track vehicle objects robustly and efficiently. To build 3D car models in a fully unsupervised manner, we also implement evolutionary camera self-calibration from tracking of walking humans to automatically compute camera parameters. Additionally, the segmented foreground masks which are crucial to 3D modeling and camera self-calibration are adaptively refined by multiple-kernel feedback from tracking. For object detection/classification, the state-of-the-art single shot multibox detector (SSD) is adopted to train and test on the NVIDIA AI City Dataset. To improve the accuracy on categories with only few objects, like bus, bicycle and motorcycle, we also employ the pretrained model from YOLO9000 with multi-scale testing. We combine the results from SSD and YOLO9000 based on ensemble learning. Experiments show that our proposed tracking system outperforms both state-of-the-art of tracking by segmentation and tracking by detection.

cs.CV↗

arXiv2016

Smoothing analysis of HLSII storage ring magnets

In order to improve the quality and stability of synchrotron light, Hefei Light Source has a major upgrade. Higher accuracy is necessary for installation and alignment of the storage ring magnets. It is not necessarily essential that the magnets are positioned exactly. In fact, the aim is to adjust neighboring magnets with a high accuracy to one another; in other words, these neighboring magnets are positioned on a smoothing curve. The paper presents an attempt to develop a reliable smoothing method based on curve fitting of least squares and iteration according to the structure characteristics of HLSII. The method significantly reduces the adjusting amount and range of the storage ring magnets. It improves productivity by a factor of one times.

physics.acc-ph↗

arXiv2011

Giant-diamagnetic and magnetization-step effects in HgMnTe monocrystal

In Hg$_{1-x}$Mn$_x$Te (x$\geq$0.16) monocrystal, the giant-diamagnetic (GDM) and magnetization-step phenomena have been observed in spin glass (SG) regime. The susceptibility of GDM is about 100-1000 times than that of classic diamagnetic. It can be interpreted that: due to the long-range antiferromagnetic (AF) exchange interactions and the non-uniform random distribution of Mn$^{2+}$ ions in Hg$_{1-x}$Mn$_x$Te, a quasi-static spin wave forms and produces the GDM phenomenon below the critical temperature and magnetic field. Meanwhile, this theory is proved by Monte Carlo simulations in a two-dimensional AF cluster based on XY model. Hence, it is possible to emerge long-range magnetic order structure in SG state.

cond-mat.mtrl-sci↗