18,564 research outputs found
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnets)
for the task of pedestrian detection. Despite their recent diverse successes,
convnets historically underperform compared to other pedestrian detectors. We
deliberately omit explicitly modelling the problem into the network (e.g. parts
or occlusion modelling) and show that we can reach competitive performance
without bells and whistles. In a wide range of experiments we analyse small and
big convnets, their architectural choices, parameters, and the influence of
different training data, including pre-training on surrogate tasks.
We present the best convnet detectors on the Caltech and KITTI dataset. On
Caltech our convnets reach top performance both for the Caltech1x and
Caltech10x training setup. Using additional data at training time our strongest
convnet model is competitive even to detectors that use additional data
(optical flow) at test time
Interacting Unities: An Agent-Based System
Recently architects have been inspired by Thompsonis Cartesian deformations and Waddingtonis flexible topological surface to work within a dynamic field characterized by forces. In this more active space of interactions, movement is the medium through which form evolves. This paper explores the interaction between pedestrians and their environment by regarding it as a process occurring between the two. It is hypothesized that the recurrent interaction between pedestrians and environment can lead to a structural coupling between those elements. Every time a change occurs in each one of them, as an expression of its own structural dynamics, it triggers changes to the other one. An agent-based system has been developed in order to explore that interaction, where the two interacting elements, agents (pedestrians) and environment, are autonomous units with a set of internal rules. The result is a landscape where each agent locally modifies its environment that in turn affects its movement, while the other agents respond to the new environment at a later time, indicating that the phenomenon of stigmergy is possible to take place among interactions with human analogy. It is found that it is the environmentis internal rules that determine the nature and extent of change
A qualitative investigation of older pedestrian views of influences on their road crossing safety
With Australia’s population rapidly ageing, older pedestrian safety has begun to receive greater attention from road safety researchers. However, reliance on simulator studies and observational techniques has limited current understanding of why older pedestrians adopt particular crossing behaviours, and how they perceive crossing the road. The current study aimed to investigate the psychological factors that may contribute to older pedestrians’ crash risk by examining their perceptions of the issues they encounter on the road. Qualitative semi-structured interviews with 18 pedestrians aged 55 years and older were conducted, and the interview transcripts underwent thematic analysis. From this analysis, four key themes emerged. Firstly, the physical design of the road was perceived as posing a significant threat for older pedestrians, particularly sloped, semi-mountable kerbs and designated crossings. Secondly, declines in older pedestrians’ confidence in their ability to cross the road were evident through fewer reported risks being taken. Additionally, older pedestrians sensed an increased threat from other road users when crossing the road, particularly from drivers and cyclists. Finally, older pedestrians referred to the informal rules and strategies used to guide their road crossing. The results suggest that the road environment is perceived as increasingly dangerous and hazardous environment for older pedestrians. Implications regarding the physical road design in areas with an existing high proportion of elderly people are discussed
Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
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