9,856 research outputs found
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
Pedestrian classifiers decide which image windows contain a pedestrian. In
practice, such classifiers provide a relatively high response at neighbor
windows overlapping a pedestrian, while the responses around potential false
positives are expected to be lower. An analogous reasoning applies for image
sequences. If there is a pedestrian located within a frame, the same pedestrian
is expected to appear close to the same location in neighbor frames. Therefore,
such a location has chances of receiving high classification scores during
several frames, while false positives are expected to be more spurious. In this
paper we propose to exploit such correlations for improving the accuracy of
base pedestrian classifiers. In particular, we propose to use two-stage
classifiers which not only rely on the image descriptors required by the base
classifiers but also on the response of such base classifiers in a given
spatiotemporal neighborhood. More specifically, we train pedestrian classifiers
using a stacked sequential learning (SSL) paradigm. We use a new pedestrian
dataset we have acquired from a car to evaluate our proposal at different frame
rates. We also test on a well known dataset: Caltech. The obtained results show
that our SSL proposal boosts detection accuracy significantly with a minimal
impact on the computational cost. Interestingly, SSL improves more the accuracy
at the most dangerous situations, i.e. when a pedestrian is close to the
camera.Comment: 8 pages, 5 figure, 1 tabl
Random convolution ensembles
A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective
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
Unsupervised learning of generative topic saliency for person re-identification
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
- …