Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles

By Bastian Leibe, Konrad Schindler, Nico Cornelis, Luc Van Gool. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

385 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b6d31905b671e6d442311c0e275772652df3abb6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, methodology): “The last section considers tracking, which can equally be applied after both classification methods.”
Visual tracking decomposition (2010, background): “Recently, many researchers have addressed the problem in real-world scenarios rather than a lab environment [13, 19].”
Robust Multiperson Tracking from a Mobile Platform (2009, methodology): “For tracking, we employ a slightly adapted version of the multiobject tracking-by-detection framework from [ 31 ].”
Multi-view traffic sign detection, recognition, and 3D localisation (2011, background): “Sd is the part of the hypothesis which is explained by the supporting candidates, Sm is the cost of coding the model itself, while Se represents those parts that are not explaining the given hypothesis, and k1, k2 are weights (as in [10]).”
State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation (2009, background): “Examples are in the use of laser scanning and photogrammetry [45–47] and in the use of stereo vision and photogrammetry for facade reconstruction and object tracking [48, 50].”
Multiple Viewpoint Recognition and Localization (2010, neutral): “…targeted for outdoor navigation such as intelligent driving systems (see several recent vision based methods among far too many others too mention [10, 11]), however in most cases these systems have attempted to recognize just a few of the most relevant object categories for the task of safe…”
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies (2017, methodology): “In recent years, tracking has been successfully extended to scenarios with multiple targets [54, 40, 26, 78].”
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments (2010, methodology): “Trajectory generation is performed in parallel from all new detections (in contrast to Leibe et al. (2008b)).”
You'll never walk alone: Modeling social behavior for multi-target tracking (2009, methodology): “To this end, researchers have focused on improving the appearance model [10, 5], the object detector [2, 7, 9, 22], and/or the optimization strategy [14, 16 , 23].”
Multi-scale object candidates for generic object tracking in street scenes (2016, methodology): “[34] and our extension of the approach, that enables us to perform”
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera (2011, methodology): “Several recent algorithms address this problem by optimizing detection assignments over a large temporal window in an offline step [1], [3], [26], [30].”
SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (2015, neutral): “Finally, we use the estimated camera motion, the ground plane and the detections from both detectors for tracking based on Leibe et al (2008) (see Fig.”
People tracking in RGB-D data with on-line boosted target models (2011, background): “The work of [13], [14] detect people in intensity images and track them in 3D.”
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures (2012, methodology): “These methods might be placed into a few broad categories: those that aim to distinguish the foreground regions of images from the background [33, 12, 65, 39], segment images into distinct regions to perform localization [35, 23, 56], track an object over a sequence of images (after its position has”