9,824 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
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a āshotā based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ābroadcastā based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
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