121 research outputs found
Crosstalk Cascades for Frame-rate Pedestrian Detection
Cascades help make sliding window object detection fast,
nevertheless, computational demands remain prohibitive for numerous applications. Currently, evaluation of adjacent windows proceeds independently; this is suboptimal as detector responses at nearby locations and scales are correlated. We propose to exploit these correlations by
tightly coupling detector evaluation of nearby windows. We introduce two opposing mechanisms: detector excitation of promising neighbors and inhibition of inferior neighbors. By enabling neighboring detectors to communicate, crosstalk cascades achieve major gains (4-30x speedup) over cascades evaluated independently at each image location. Combined
with recent advances in fast multi-scale feature computation, for which we provide an optimized implementation, our approach runs at 35-65 fps
on 640 x 480 images while attaining state-of-the-art accuracy
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
Fast traffic sign recognition using color segmentation and deep convolutional networks
The use of Computer Vision techniques for the automatic
recognition of road signs is fundamental for the development of intelli-
gent vehicles and advanced driver assistance systems. In this paper, we
describe a procedure based on color segmentation, Histogram of Ori-
ented Gradients (HOG), and Convolutional Neural Networks (CNN) for
detecting and classifying road signs. Detection is speeded up by a pre-
processing step to reduce the search space, while classication is carried
out by using a Deep Learning technique. A quantitative evaluation of the
proposed approach has been conducted on the well-known German Traf-
c Sign data set and on the novel Data set of Italian Trac Signs (DITS),
which is publicly available and contains challenging sequences captured
in adverse weather conditions and in an urban scenario at night-time.
Experimental results demonstrate the eectiveness of the proposed ap-
proach in terms of both classication accuracy and computational speed
Deteksi Pejalan Kaki Pada Video Dengan Metode Fastest Pedestrian Detector in the West (FPDW)
— Pedestrian detection in video can be done offline for the purposes of analysis, such as analysis of pedestrian behavior, riots analysis and analysis of traffic accidents. In addition, pedestrian detection process is also done in real-time, such as for driving security systems, indoor navigation system, traffic control system at crossroads and so on. In this paper, a method FPDW (Fastest Pedestrian Detector in the West) will be tested to detect pedestrians in a video. Contribution of this paper is to add parameter of minimum confidence to the FPDW algorithm, to improve the accuracy of detection results. Experiments conducted on the video duration of 6 minutes 50 seconds extracted into 1 image per second.Keywords— pedestrian detector, FPDW, HOG, vide
Aggregated Channels Network for Real-Time Pedestrian Detection
Convolutional neural networks (CNNs) have demonstrated their superiority in
numerous computer vision tasks, yet their computational cost results
prohibitive for many real-time applications such as pedestrian detection which
is usually performed on low-consumption hardware. In order to alleviate this
drawback, most strategies focus on using a two-stage cascade approach.
Essentially, in the first stage a fast method generates a significant but
reduced amount of high quality proposals that later, in the second stage, are
evaluated by the CNN. In this work, we propose a novel detection pipeline that
further benefits from the two-stage cascade strategy. More concretely, the
enriched and subsequently compressed features used in the first stage are
reused as the CNN input. As a consequence, a simpler network architecture,
adapted for such small input sizes, allows to achieve real-time performance and
obtain results close to the state-of-the-art while running significantly faster
without the use of GPU. In particular, considering that the proposed pipeline
runs in frame rate, the achieved performance is highly competitive. We
furthermore demonstrate that the proposed pipeline on itself can serve as an
effective proposal generator
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