13,430 research outputs found
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201
Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets
Acoustic events often have a visual counterpart. Knowledge of visual
information can aid the understanding of complex auditory scenes, even when
only a stereo mixdown is available in the audio domain, \eg identifying which
musicians are playing in large musical ensembles. In this paper, we consider a
vision-based approach to note onset detection. As a case study we focus on
challenging, real-world clarinetist videos and carry out preliminary
experiments on a 3D convolutional neural network based on multiple streams and
purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5
hours of clarinetist videos together with cleaned annotations which include
about 36,000 onsets and the coordinates for a number of salient points and
regions of interest. By performing several training trials on our dataset, we
learned that the problem is challenging. We found that the CNN model is highly
sensitive to the optimization algorithm and hyper-parameters, and that treating
the problem as binary classification may prevent the joint optimization of
precision and recall. To encourage further research, we publicly share our
dataset, annotations and all models and detail which issues we came across
during our preliminary experiments.Comment: Proceedings of the First International Conference on Deep Learning
and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE]
Summarizing First-Person Videos from Third Persons' Points of Views
Video highlight or summarization is among interesting topics in computer
vision, which benefits a variety of applications like viewing, searching, or
storage. However, most existing studies rely on training data of third-person
videos, which cannot easily generalize to highlight the first-person ones. With
the goal of deriving an effective model to summarize first-person videos, we
propose a novel deep neural network architecture for describing and
discriminating vital spatiotemporal information across videos with different
points of view. Our proposed model is realized in a semi-supervised setting, in
which fully annotated third-person videos, unlabeled first-person videos, and a
small number of annotated first-person ones are presented during training. In
our experiments, qualitative and quantitative evaluations on both benchmarks
and our collected first-person video datasets are presented.Comment: 16+10 pages, ECCV 201
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
Wayfinding in Complex Multi-storey Buildings: A vision-simulation-augmented wayfinding protocol study
Wayfinding in complex multi-storey buildings often brings newcomers and even some frequent visitors uncertainty and stress. However, there is little understanding on wayfinding in 3D structure which contains inter-storey and inter-building travelling.
This paper presents the method of vision-simulation-augmented wayfinding protocol for the study of such 3D structure to find its application from investigating pedestrians’ wayfinding behaviour in general-purpose complex multi-storey buildings. Based on Passini’s studies as a starting point, an exploratory quasi-experiment was developed during the study and then conducted in a daily wayfinding context, adopting wayfinding protocol method with augmentation by the real-time vision simulation. The purpose is to identify people’s natural wayfinding strategies in natural settings, for both frequent visitors and newcomers. It is envisioned that the findings of the study can inspire potential design solutions for supporting pedestrian’s wayfinding in 3D indoor spaces.
From the new method developed and new analytic framework, several findings were identified which differ from other wayfinding literature, such as (1) people seem to directly “make sense” of wayfinding settings, (2) people could translate recurring actions into unconscious operational behaviours, and (3) physical rotation and constrained views, instead of vertical travelling itself, should be problems for wayfinding process, etc.
Keywords:
Wayfinding Protocol; Real-time Vision Simulation; 3D Indoor Space; Activity Theory; Structure of Wayfinding process</p
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