Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. However, in such visual perception pipeline the detected objects must also be tracked, in a process called Multiple Object Tracking (MOT), to build the moving trajectories of surrounding obstacles. Since MOT is designed to be robust against errors in object detection, it poses a general challenge to existing attack techniques that blindly target objection detection: we find that a success rate of over 98% is needed for them to actually affect the tracking results, a requirement that no existing attack technique can satisfy. In this paper, we are the first to study adversarial machine learning attacks against the complete visual perception pipeline in autonomous driving, and discover a novel attack technique, tracker hijacking, that can effectively fool MOT using AEs on object detection. Using our technique, successful AEs on as few as one single frame can move an existing object in to or out of the headway of an autonomous vehicle to cause potential safety hazards. We perform evaluation using the Berkeley Deep Drive dataset and find that on average when 3 frames are attacked, our attack can have a nearly 100% success rate while attacks that blindly target object detection only have up to 25%.
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Online Multi-Object Tracking with Instance-Aware Single-Object Tracking and Dynamic Model Refreshment
Recent progresses in model-free single object tracking
(SOT) algorithms have largely inspired applying SOT to
multi-object tracking (MOT) to improve the robustness as
well as relieving dependency on external detector. However,
SOT algorithms are generally designed for distinguishing a
target from its environment, and hence meet problems when
a target is spatially mixed with similar objects as observed
frequently in MOT. To address this issue, in this paper we
propose an instance-aware tracker to integrate SOT techniques
for MOT by encoding awareness both within and between
target models. In particular, we construct each target
model by fusing information for distinguishing target
both from background and other instances (tracking targets).
To conserve uniqueness of all target models, our
instance-aware tracker considers response maps from all
target models and assigns spatial locations exclusively to
optimize the overall accuracy. Another contribution we
make is a dynamic model refreshing strategy learned by a
convolutional neural network. This strategy helps to eliminate
initialization noise as well as to adapt to the variation
of target size and appearance. To show the effectiveness
of the proposed approach, it is evaluated on the popular
MOT15 and MOT16 challenge benchmarks. On both
benchmarks, our approach achieves the best overall performances
in comparison with published results.
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- Award ID(s):
- 1814745
- NSF-PAR ID:
- 10109440
- Date Published:
- Journal Name:
- IEEE Winter Conference on Applications of Computer Vision
- ISSN:
- 2472-6796
- Page Range / eLocation ID:
- 161-170
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
