1,668 research outputs found
Online Object Tracking with Proposal Selection
Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.Comment: ICCV 201
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.Comment: ACCV 201
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