Benchmarking high-fidelity pedestrian tracking systems for research,
real-time monitoring and crowd control
- URL: http://arxiv.org/abs/2108.11719v1
- Date: Thu, 26 Aug 2021 11:45:26 GMT
- Title: Benchmarking high-fidelity pedestrian tracking systems for research,
real-time monitoring and crowd control
- Authors: Caspar A. S. Pouw, Joris Willems, Frank van Schadewijk, Jasmin Thurau,
Federico Toschi, Alessandro Corbetta
- Abstract summary: High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research.
As this technology advances, it is becoming increasingly useful also in society.
To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy.
We present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques.
- Score: 55.41644538483948
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-fidelity pedestrian tracking in real-life conditions has been an
important tool in fundamental crowd dynamics research allowing to quantify
statistics of relevant observables including walking velocities, mutual
distances and body orientations. As this technology advances, it is becoming
increasingly useful also in society. In fact, continued urbanization is
overwhelming existing pedestrian infrastructures such as transportation hubs
and stations, generating an urgent need for real-time highly-accurate usage
data, aiming both at flow monitoring and dynamics understanding. To
successfully employ pedestrian tracking techniques in research and technology,
it is crucial to validate and benchmark them for accuracy. This is not only
necessary to guarantee data quality, but also to identify systematic errors.
In this contribution, we present and discuss a benchmark suite, towards an
open standard in the community, for privacy-respectful pedestrian tracking
techniques. The suite is technology-independent and is applicable to academic
and commercial pedestrian tracking systems, operating both in lab environments
and real-life conditions. The benchmark suite consists of 5 tests addressing
specific aspects of pedestrian tracking quality, including accurate crowd flux
estimation, density estimation, position detection and trajectory accuracy. The
output of the tests are quality factors expressed as single numbers. We provide
the benchmark results for two tracking systems, both operating in real-life,
one commercial, and the other based on overhead depth-maps developed at TU
Eindhoven. We discuss the results on the basis of the quality factors and
report on the typical sensor and algorithmic performance. This enables us to
highlight the current state-of-the-art, its limitations and provide
installation recommendations, with specific attention to multi-sensor setups
and data stitching.
Related papers
- Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey [10.494414329120909]
Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range.
Event-based cameras address limitations by eschewing extraneous data transmissions and obviating motion blur in high-speed imaging scenarios.
This paper offers an exhaustive review of research and applications particularly in the autonomous driving context.
arXiv Detail & Related papers (2024-07-05T06:17:00Z) - OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
Trajectory prediction is fundamental in computer vision and autonomous driving.
Existing approaches in this field often assume precise and complete observational data.
We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
arXiv Detail & Related papers (2024-04-02T18:30:29Z) - PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based
Cameras [4.110120522045467]
Event-cameras can guarantee fast visual sensing in dynamic environments, but require a tracking algorithm that can keep up with the high data rate induced by the robot ego-motion.
We introduce a novel tracking method that leverages the Exponential Reduced Ordinal Surface (EROS) data representation to decouple event-by-event processing and tracking.
We propose the task of tracking the air hockey puck sliding on a surface, with the future aim of controlling the iCub robot to reach the target precisely and on time.
arXiv Detail & Related papers (2022-05-16T13:23:52Z) - Automated Mobility Context Detection with Inertial Signals [7.71058263701836]
The primary goal of this paper is the investigation of context detection for remote monitoring of daily motor functions.
We aim to understand whether inertial signals sampled with wearable accelerometers, provide reliable information to classify gait-related activities as either indoor or outdoor.
arXiv Detail & Related papers (2022-05-16T09:34:43Z) - Scalable and Real-time Multi-Camera Vehicle Detection,
Re-Identification, and Tracking [58.95210121654722]
We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams.
Our method is ranked among the top five performers on the public leaderboard.
arXiv Detail & Related papers (2022-04-15T12:47:01Z) - Pedestrian Detection: Domain Generalization, CNNs, Transformers and
Beyond [82.37430109152383]
We show that, current pedestrian detectors poorly handle even small domain shifts in cross-dataset evaluation.
We attribute the limited generalization to two main factors, the method and the current sources of data.
We propose a progressive fine-tuning strategy which improves generalization.
arXiv Detail & Related papers (2022-01-10T06:00:26Z) - Predictive Visual Tracking: A New Benchmark and Baseline Approach [27.87099869398515]
In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states.
Existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation.
In this work, we aim to deal with a more realistic problem of latency-aware tracking.
arXiv Detail & Related papers (2021-03-08T01:50:05Z) - From Handcrafted to Deep Features for Pedestrian Detection: A Survey [148.35460817092908]
Pedestrian detection is an important but challenging problem in computer vision.
Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features.
In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection.
arXiv Detail & Related papers (2020-10-01T14:51:10Z) - Generalizable Pedestrian Detection: The Elephant In The Room [82.37430109152383]
We find that existing state-of-the-art pedestrian detectors, though perform quite well when trained and tested on the same dataset, generalize poorly in cross dataset evaluation.
We illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
arXiv Detail & Related papers (2020-03-19T14:14:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.