4,966 research outputs found
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
HOG, LBP and SVM based Traffic Density Estimation at Intersection
Increased amount of vehicular traffic on roads is a significant issue. High
amount of vehicular traffic creates traffic congestion, unwanted delays,
pollution, money loss, health issues, accidents, emergency vehicle passage and
traffic violations that ends up in the decline in productivity. In peak hours,
the issues become even worse. Traditional traffic management and control
systems fail to tackle this problem. Currently, the traffic lights at
intersections aren't adaptive and have fixed time delays. There's a necessity
of an optimized and sensible control system which would enhance the efficiency
of traffic flow. Smart traffic systems perform estimation of traffic density
and create the traffic lights modification consistent with the quantity of
traffic. We tend to propose an efficient way to estimate the traffic density on
intersection using image processing and machine learning techniques in real
time. The proposed methodology takes pictures of traffic at junction to
estimate the traffic density. We use Histogram of Oriented Gradients (HOG),
Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for
traffic density estimation. The strategy is computationally inexpensive and can
run efficiently on raspberry pi board. Code is released at
https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201
Detection thresholding using mutual information
In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information
Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the
mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our
idea are presented: one using dynamic programming to fully explore the quantised search space and the other
method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the
assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection
(using multi-modal data) and as a component in a person detection system
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
201
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