237 research outputs found
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnets)
for the task of pedestrian detection. Despite their recent diverse successes,
convnets historically underperform compared to other pedestrian detectors. We
deliberately omit explicitly modelling the problem into the network (e.g. parts
or occlusion modelling) and show that we can reach competitive performance
without bells and whistles. In a wide range of experiments we analyse small and
big convnets, their architectural choices, parameters, and the influence of
different training data, including pre-training on surrogate tasks.
We present the best convnet detectors on the Caltech and KITTI dataset. On
Caltech our convnets reach top performance both for the Caltech1x and
Caltech10x training setup. Using additional data at training time our strongest
convnet model is competitive even to detectors that use additional data
(optical flow) at test time
Real-Time RGB-D based Template Matching Pedestrian Detection
Pedestrian detection is one of the most popular topics in computer vision and
robotics. Considering challenging issues in multiple pedestrian detection, we
present a real-time depth-based template matching people detector. In this
paper, we propose different approaches for training the depth-based template.
We train multiple templates for handling issues due to various upper-body
orientations of the pedestrians and different levels of detail in depth-map of
the pedestrians with various distances from the camera. And, we take into
account the degree of reliability for different regions of sliding window by
proposing the weighted template approach. Furthermore, we combine the
depth-detector with an appearance based detector as a verifier to take
advantage of the appearance cues for dealing with the limitations of depth
data. We evaluate our method on the challenging ETH dataset sequence. We show
that our method outperforms the state-of-the-art approaches.Comment: published in ICRA 201
Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling
The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: Institut National de Recherche en Sciences et Technologies du Numérique (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples
Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models
Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology
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