3,266 research outputs found
Hashmod: A Hashing Method for Scalable 3D Object Detection
We present a scalable method for detecting objects and estimating their 3D
poses in RGB-D data. To this end, we rely on an efficient representation of
object views and employ hashing techniques to match these views against the
input frame in a scalable way. While a similar approach already exists for 2D
detection, we show how to extend it to estimate the 3D pose of the detected
objects. In particular, we explore different hashing strategies and identify
the one which is more suitable to our problem. We show empirically that the
complexity of our method is sublinear with the number of objects and we enable
detection and pose estimation of many 3D objects with high accuracy while
outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter
Reference face graph for face recognition
Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
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