One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.
New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.
Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion.
This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the
tracking performance. Several works address this problem using ego-motion compensation strategies. They use a
deterministic approach to compensate the camera motion assuming a specific model of geometric transformation.
However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion
for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly
decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on
the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a
novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations:
Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the
object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter
using the appearance information. This approach is able to adapt to different camera ego-motion conditions,
and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR
dataset, showing a high efficiency in the tracking of different types of targets in real working conditions.
Motion estimation in video sequences is a classical intensive computational task that is required for a wide
range of applications. Many different methods have been proposed to reduce the computational complexity,
but the achieved reduction is not enough to allow real time operation in a non-specialized hardware. In this
paper an efficient selection of singular points for fast matching between consecutive images is presented, which
allows to achieve real time operation. The selection of singular points lies in finding the image points that
are robust to the noise and the aperture problem. This is accomplished by imposing restrictions related to
the gradient magnitude and the cornerness. The neighborhood of each singular point is characterized by a
complex descriptor vector, which presents a high robustness to illumination changes and small variations in the
3D camera viewpoint. The matching between singular points of consecutive images is performed by maximizing
a similarity measure based on the previous descriptor vector. The set of correspondences yields a sparse motion
vector field that accurately outlines the image motion. In order to demonstrate the efficiency of this approach, a
video stabilization application has been developed, which uses the sparse motion vector field as input. Excellent
results have been efficiency of the proposed motion
estimation technique.
Common strategies for detection and tracking of aerial moving targets in airborne Forward-Looking Infrared
(FLIR) images offer accurate results in images composed by a
non-textured sky. However, when cloud and
earth regions appear in the image sequence, those strategies result in an over-detection that increases very
signficantly the false alarm rate. Besides, the airborne camera induces a global motion in the image sequence
that complicates even more detection and tracking tasks. In this work, an automatic detection and tracking
system with an innovative and efficient target trajectory filtering is presented. It robustly compensates the
global motion to accurately detect and track potential aerial targets. Their trajectories are analyzed by a curve
fitting technique to reliably validate real targets. This strategy allows to filter false targets with stationary or
erratic trajectories. The proposed system makes special emphasis in the use of low complexity video analysis
techniques to achieve real-time operation. Experimental results using real FLIR sequences show a dramatic
reduction of the false alarm rate, while maintaining the detection rate.
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