82,280 research outputs found
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Advances in perception for self-driving cars have accelerated in recent years
due to the availability of large-scale datasets, typically collected at
specific locations and under nice weather conditions. Yet, to achieve the high
safety requirement, these perceptual systems must operate robustly under a wide
variety of weather conditions including snow and rain. In this paper, we
present a new dataset to enable robust autonomous driving via a novel data
collection process - data is repeatedly recorded along a 15 km route under
diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time
(day/night), and traffic conditions (pedestrians, cyclists and cars). The
dataset includes images and point clouds from cameras and LiDAR sensors, along
with high-precision GPS/INS to establish correspondence across routes. The
dataset includes road and object annotations using amodal masks to capture
partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this
dataset by analyzing the performance of baselines in amodal segmentation of
road and objects, depth estimation, and 3D object detection. The repeated
routes opens new research directions in object discovery, continual learning,
and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/Comment: Accepted by CVPR 202
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
The task of a visual landmark recognition system is to identify photographed
buildings or objects in query photos and to provide the user with relevant
information on them. With their increasing coverage of the world's landmark
buildings and objects, Internet photo collections are now being used as a
source for building such systems in a fully automatic fashion. This process
typically consists of three steps: clustering large amounts of images by the
objects they depict; determining object names from user-provided tags; and
building a robust, compact, and efficient recognition index. To this date,
however, there is little empirical information on how well current approaches
for those steps perform in a large-scale open-set mining and recognition task.
Furthermore, there is little empirical information on how recognition
performance varies for different types of landmark objects and where there is
still potential for improvement. With this paper, we intend to fill these gaps.
Using a dataset of 500k images from Paris, we analyze each component of the
landmark recognition pipeline in order to answer the following questions: How
many and what kinds of objects can be discovered automatically? How can we best
use the resulting image clusters to recognize the object in a query? How can
the object be efficiently represented in memory for recognition? How reliably
can semantic information be extracted? And finally: What are the limiting
factors in the resulting pipeline from query to semantics? We evaluate how
different choices of methods and parameters for the individual pipeline steps
affect overall system performance and examine their effects for different query
categories such as buildings, paintings or sculptures
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects.Comment: ICRA'18 submissio
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