4,242 research outputs found
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction
Anomaly detection from a driver's perspective when driving is important to
autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it
can remind the driver about dangers timely. Compared with traditional studied
scenes such as the university campus and market surveillance videos, it is
difficult to detect abnormal event from a driver's perspective due to camera
waggle, abidingly moving background, drastic change of vehicle velocity, etc.
To tackle these specific problems, this paper proposes a spatial localization
constrained sparse coding approach for anomaly detection in traffic scenes,
which firstly measures the abnormality of motion orientation and magnitude
respectively and then fuses these two aspects to obtain a robust detection
result. The main contributions are threefold: 1) This work describes the motion
orientation and magnitude of the object respectively in a new way, which is
demonstrated to be better than the traditional motion descriptors. 2) The
spatial localization of object is taken into account of the sparse
reconstruction framework, which utilizes the scene's structural information and
outperforms the conventional sparse coding methods. 3) Results of motion
orientation and magnitude are adaptively weighted and fused by a Bayesian
model, which makes the proposed method more robust and handle more kinds of
abnormal events. The efficiency and effectiveness of the proposed method are
validated by testing on nine difficult video sequences captured by ourselves.
Observed from the experimental results, the proposed method is more effective
and efficient than the popular competitors, and yields a higher performance.Comment: IEEE Transactions on Intelligent Transportation System
Saliency Guided Hierarchical Robust Visual Tracking
A saliency guided hierarchical visual tracking (SHT) algorithm containing
global and local search phases is proposed in this paper. In global search, a
top-down saliency model is novelly developed to handle abrupt motion and
appearance variation problems. Nineteen feature maps are extracted first and
combined with online learnt weights to produce the final saliency map and
estimated target locations. After the evaluation of integration mechanism, the
optimum candidate patch is passed to the local search. In local search, a
superpixel based HSV histogram matching is performed jointly with an L2-RLS
tracker to take both color distribution and holistic appearance feature of the
object into consideration. Furthermore, a linear refinement search process with
fast iterative solver is implemented to attenuate the possible negative
influence of dominant particles. Both qualitative and quantitative experiments
are conducted on a series of challenging image sequences. The superior
performance of the proposed method over other state-of-the-art algorithms is
demonstrated by comparative study
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
We propose an online visual tracking algorithm by learning discriminative
saliency map using Convolutional Neural Network (CNN). Given a CNN pre-trained
on a large-scale image repository in offline, our algorithm takes outputs from
hidden layers of the network as feature descriptors since they show excellent
representation performance in various general visual recognition problems. The
features are used to learn discriminative target appearance models using an
online Support Vector Machine (SVM). In addition, we construct target-specific
saliency map by backpropagating CNN features with guidance of the SVM, and
obtain the final tracking result in each frame based on the appearance model
generatively constructed with the saliency map. Since the saliency map
visualizes spatial configuration of target effectively, it improves target
localization accuracy and enable us to achieve pixel-level target segmentation.
We verify the effectiveness of our tracking algorithm through extensive
experiment on a challenging benchmark, where our method illustrates outstanding
performance compared to the state-of-the-art tracking algorithms
Scale Selection of Adaptive Kernel Regression by Joint Saliency Map for Nonrigid Image Registration
Joint saliency map (JSM) [1] was developed to assign high joint saliency
values to the corresponding saliency structures (called Joint Saliency
Structures, JSSs) but zero or low joint saliency values to the outliers (or
mismatches) that are introduced by missing correspondence or local large
deformations between the reference and moving images to be registered. JSM
guides the local structure matching in nonrigid registration by emphasizing
these JSSs' sparse deformation vectors in adaptive kernel regression of
hierarchical sparse deformation vectors for iterative dense deformation
reconstruction. By designing an effective superpixel-based local structure
scale estimator to compute the reference structure's structure scale, we
further propose to determine the scale (the width) of kernels in the adaptive
kernel regression through combining the structure scales to JSM-based scales of
mismatch between the local saliency structures. Therefore, we can adaptively
select the sample size of sparse deformation vectors to reconstruct the dense
deformation vectors for accurately matching the every local structures in the
two images. The experimental results demonstrate better accuracy of our method
in aligning two images with missing correspondence and local large deformation
than the state-of-the-art methods.Comment: 9 page
SalProp: Salient object proposals via aggregated edge cues
In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. A Conditional Random Field is then learned to effectively
combine these features for edge classification with object/non-object label. We
propose an objectness score for the generated windows by analyzing the salient
edge density inside the bounding box. Extensive experiments on PASCAL VOC 2007
dataset demonstrate that the proposed method gives competitive performance
against 10 popular generic object detection techniques while using fewer number
of proposals.Comment: 5 pages, 4 figures, accepted at ICIP 201
Bayesian Fusion for Infrared and Visible Images
Infrared and visible image fusion has been a hot issue in image fusion. In
this task, a fused image containing both the gradient and detailed texture
information of visible images as well as the thermal radiation and highlighting
targets of infrared images is expected to be obtained. In this paper, a novel
Bayesian fusion model is established for infrared and visible images. In our
model, the image fusion task is cast into a regression problem. To measure the
variable uncertainty, we formulate the model in a hierarchical Bayesian manner.
Aiming at making the fused image satisfy human visual system, the model
incorporates the total-variation(TV) penalty. Subsequently, the model is
efficiently inferred by the expectation-maximization(EM) algorithm. We test our
algorithm on TNO and NIR image fusion datasets with several state-of-the-art
approaches. Compared with the previous methods, the novel model can generate
better fused images with high-light targets and rich texture details, which can
improve the reliability of the target automatic detection and recognition
system
Salient Object Detection: A Distinctive Feature Integration Model
We propose a novel method for salient object detection in different images.
Our method integrates spatial features for efficient and robust representation
to capture meaningful information about the salient objects. We then train a
conditional random field (CRF) using the integrated features. The trained CRF
model is then used to detect salient objects during the online testing stage.
We perform experiments on two standard datasets and compare the performance of
our method with different reference methods. Our experiments show that our
method outperforms the compared methods in terms of precision, recall, and
F-Measure
A Classifier-guided Approach for Top-down Salient Object Detection
We propose a framework for top-down salient object detection that
incorporates a tightly coupled image classification module. The classifier is
trained on novel category-aware sparse codes computed on object dictionaries
used for saliency modeling. A misclassification indicates that the
corresponding saliency model is inaccurate. Hence, the classifier selects
images for which the saliency models need to be updated. The category-aware
sparse coding produces better image classification accuracy as compared to
conventional sparse coding with a reduced computational complexity. A
saliency-weighted max-pooling is proposed to improve image classification,
which is further used to refine the saliency maps. Experimental results on
Graz-02 and PASCAL VOC-07 datasets demonstrate the effectiveness of salient
object detection. Although the role of the classifier is to support salient
object detection, we evaluate its performance in image classification and also
illustrate the utility of thresholded saliency maps for image segmentation.Comment: To appear in Signal Processing: Image Communication, Elsevier.
Available online from April 201
Fast 3D Salient Region Detection in Medical Images using GPUs
Automated detection of visually salient regions is an active area of research
in computer vision. Salient regions can serve as inputs for object detectors as
well as inputs for region based registration algorithms. In this paper we
consider the problem of speeding up computationally intensive bottom-up salient
region detection in 3D medical volumes.The method uses the Kadir Brady
formulation of saliency. We show that in the vicinity of a salient region,
entropy is a monotonically increasing function of the degree of overlap of a
candidate window with the salient region. This allows us to initialize a sparse
seed-point grid as the set of tentative salient region centers and iteratively
converge to the local entropy maxima, thereby reducing the computation
complexity compared to the Kadir Brady approach of performing this computation
at every point in the image. We propose two different approaches for achieving
this. The first approach involves evaluating entropy in the four quadrants
around the seed point and iteratively moving in the direction that increases
entropy. The second approach we propose makes use of mean shift tracking
framework to affect entropy maximizing moves. Specifically, we propose the use
of uniform pmf as the target distribution to seek high entropy regions. We
demonstrate the use of our algorithm on medical volumes for left ventricle
detection in PET images and tumor localization in brain MR sequences.Comment: 9 page
ROSA: Robust Salient Object Detection against Adversarial Attacks
Recently salient object detection has witnessed remarkable improvement owing
to the deep convolutional neural networks which can harvest powerful features
for images. In particular, state-of-the-art salient object detection methods
enjoy high accuracy and efficiency from fully convolutional network (FCN) based
frameworks which are trained from end to end and predict pixel-wise labels.
However, such framework suffers from adversarial attacks which confuse neural
networks via adding quasi-imperceptible noises to input images without changing
the ground truth annotated by human subjects. To our knowledge, this paper is
the first one that mounts successful adversarial attacks on salient object
detection models and verifies that adversarial samples are effective on a wide
range of existing methods. Furthermore, this paper proposes a novel end-to-end
trainable framework to enhance the robustness for arbitrary FCN-based salient
object detection models against adversarial attacks. The proposed framework
adopts a novel idea that first introduces some new generic noise to destroy
adversarial perturbations, and then learns to predict saliency maps for input
images with the introduced noise. Specifically, our proposed method consists of
a segment-wise shielding component, which preserves boundaries and destroys
delicate adversarial noise patterns and a context-aware restoration component,
which refines saliency maps through global contrast modeling. Experimental
results suggest that our proposed framework improves the performance
significantly for state-of-the-art models on a series of datasets.Comment: To be published in IEEE Transactions on Cybernetic
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