3,364 research outputs found
3D Object Class Detection in the Wild
Object class detection has been a synonym for 2D bounding box localization
for the longest time, fueled by the success of powerful statistical learning
techniques, combined with robust image representations. Only recently, there
has been a growing interest in revisiting the promise of computer vision from
the early days: to precisely delineate the contents of a visual scene, object
by object, in 3D. In this paper, we draw from recent advances in object
detection and 2D-3D object lifting in order to design an object class detector
that is particularly tailored towards 3D object class detection. Our 3D object
class detection method consists of several stages gradually enriching the
object detection output with object viewpoint, keypoints and 3D shape
estimates. Following careful design, in each stage it constantly improves the
performance and achieves state-ofthe-art performance in simultaneous 2D
bounding box and viewpoint estimation on the challenging Pascal3D+ dataset
Object Detection based on Region Decomposition and Assembly
Region-based object detection infers object regions for one or more
categories in an image. Due to the recent advances in deep learning and region
proposal methods, object detectors based on convolutional neural networks
(CNNs) have been flourishing and provided the promising detection results.
However, the detection accuracy is degraded often because of the low
discriminability of object CNN features caused by occlusions and inaccurate
region proposals. In this paper, we therefore propose a region decomposition
and assembly detector (R-DAD) for more accurate object detection.
In the proposed R-DAD, we first decompose an object region into multiple
small regions. To capture an entire appearance and part details of the object
jointly, we extract CNN features within the whole object region and decomposed
regions. We then learn the semantic relations between the object and its parts
by combining the multi-region features stage by stage with region assembly
blocks, and use the combined and high-level semantic features for the object
classification and localization. In addition, for more accurate region
proposals, we propose a multi-scale proposal layer that can generate object
proposals of various scales. We integrate the R-DAD into several feature
extractors, and prove the distinct performance improvement on PASCAL07/12 and
MSCOCO18 compared to the recent convolutional detectors.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligence (AAAI
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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