| Records |
| Author |
Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
| Title |
Temporal Coherence Analysis for Intelligent Headlight Control |
Type |
Miscellaneous |
| Year |
2008 |
Publication |
2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
59–64 |
| Keywords |
Intelligent Headlights |
| Abstract |
|
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title  |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
IROS |
| Notes |
ADAS;CIC |
Approved |
no |
| Call Number |
ADAS @ adas @ LHB2008b |
Serial |
1112 |
| Permanent link to this record |
| |
|
| |
| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
| Title |
Multiple-target tracking for the intelligent headlights control |
Type |
Conference Article |
| Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
903–910 |
| Keywords |
Intelligent Headlights |
| Abstract |
TA7.4
Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
| Address |
Madeira Island (Portugal) |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
|
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
ITSC |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RSL2010 |
Serial |
1422 |
| Permanent link to this record |
| |
|
| |
| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
| Title |
Multiple target tracking for intelligent headlights control |
Type |
Journal Article |
| Year |
2012 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
13 |
Issue |
2 |
Pages |
594-605 |
| Keywords |
Intelligent Headlights |
| Abstract |
Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
| Address |
|
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
|
Editor |
|
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1524-9050 |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g |
Serial |
1877 |
| Permanent link to this record |
