Research on pedestrian detection technology based on improved DPM model

Pedestrian detection is one of the most popular computer vision challenges in the automotive, security and domotics industries, with several new approaches and benchmarks proposed every year. All of them typically consider the pedestrians in a standing pose, but this assumption is not always applicable. It is the case of embedded camera systems used for crowd monitoring or in driving assistance systems for big vehicles maneuvering. Such systems are commonly installed as higher as possible and make use of fish-eye lenses to provide a top and wide field of view. Actually, such configurations introduce both perspective and optical distortions in the image that, even when corrected, still provide stretched silhouettes that can hardly be detected by cutting-edge pedestrian detection algorithms. In this paper we focus on this scenario, showing a) that one of the most effective models for pedestrian detection, that is the Deformable Part Model (DPM), can be efficiently implemented in FPGA to dramatically speed up the computation, and b) how it can be modified for dealing with highly distorted pictures of humans. The resulting framework, dubbed Deformable Part Model for Local Spatial Deformations (DPM-LSD), gives convincing figure of merits in terms of accuracy and throughput, on a new top-view fish-eye based pedestrian dataset (dubbed Fish-Eyed Pedestrians), also comparing with widely-used competitors (standard DPM and Dalal-Triggs).

The advanced intelligent driving assistance system has improved the current traffic congestion to a great extent and effectively reduced frequent traffic safety accidents. Pedestrian detection technology is the core of autonomous driving technology, and its accuracy, real-time and complexity will directly determine the safe operation of autonomous driving. In the case of heavy traffic, detecting a single pedestrian in a crowd is still a challenging problem. Considering the problem of mutual occlusion between pedestrians in dense crowds, an improved function algorithm based on YOLOv3 is proposed to optimize the loss function and increase the accuracy of detection by replacing the anchor frame. Experimental results show that this method can effectively reduce the missed detection rate, increase the average accuracy, and help improve the effectiveness of pedestrian occlusion detection, ensure accurate pedestrian detection in traffic congestion scenarios, and ensure driving safety.KeywordsComputer visionDeep learningPedestrian detectionLoss function

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

Automatic object detection is a rapidly evolving area in surveillance and autonomous vehicles. Deformable part model (DPM) is a well-known object detector for its high precision and speed bottleneck. This paper proposes a very fast object detection pipeline based on complementary techniques to accelerate DPM. A recent fast feature pyramid technique is employed with look-up table HOG features, Fast Fourier Transform and early classification technique to speed up DPM and maintain its accuracy. We exploit SIMD optimization and multiple cores to achieve a real time detector. Our results shows that we achieve a speed-up of 50x and 6x on a single core over DPM and cascade DPM respectively. Our optimized version processes a 640×480 pixel image at 38 fps.

The majority of pedestrian detection approaches use multiscale detection and the sliding window search scheme with high computing complexity. We present a fast pedestrian detection method using the deformable part model and pyramid layer location (PLL). First, the object proposal method is used rather than the traditional sliding window to obtain pedestrian proposal regions. Then, a PLL method is proposed to select the optimal root level in the feature pyramid for each candidate window. On this basis, a single-point calculation scheme is designed to calculate the scores of candidate windows efficiently. Finally, pedestrians can be located from the images. The Institut national de recherche en informatique et en automatique dataset for human detection is used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method can reduce the number of feature maps and windows requiring calculation in the detection process. Consequently, the computing cost is significantly reduced, with fewer false positives.

Pedestrian detection technology is the use of computer vision technology to determine whether there are pedestrians in static images or video images, and accurately mark pedestrians. Intelligent vehicles, intelligent monitoring, human behaviour analysis and other aspects are related to pedestrian detection technology, so pedestrian detection technology has been widely concerned by all walks of life. However, in some application environments, the network setting is flexible and the location of the device can be changed at any time, so it is difficult to establish a pedestrian detection system by wired way, and the pedestrian detection system can only be established through WSN. It is necessary to consider how to complete pedestrian detection through intelligent terminal under the condition of limited computing power. Many researchers have put forward a lot of algorithms, which are constantly optimized, but there are still many problems to be solved in practical application. In this paper, aiming at the intelligent terminal in WSN environment with limited computing power, a pedestrian detection system based on HOG+SVM is designed, which can detect moving pedestrians in video in real time. The detection system is mainly composed of detection algorithm and video acquisition module. After the test of INRIA and CVC data set, the accuracy of the detection algorithm in this paper is 89.06%. This method is tested under the intelligent terminal with limited computing power, with a good detection effect.

Tracking of previously unseen, articulated objects is an active research area. Recently, Deformable Parts Models (DPMs) have been used to improve the online tracking performance for bounding-box trackers. In this paper, we extend the DPM with global priors which enforce consistency with foreground/background segmentation cues. We propose a Dual Decomposition approach and show how to efficiently solve the high-order coupling constraints as a feasible sub-problem. The proposed approach is evaluated on the VOT online tracking benchmark, outperforming the baseline in both tracking accuracy and robustness. We further show that in presence of stable image segmentation cues, the flexibility of a generic DPM generated from a single reference frame can be improved by introducing the concept of part visibility, the visibility-aware DPM (VDPM). This allows for fine-grained articulated object tracking using an automatically generated DPM from a single template image.

In complex scene, considering traditional object detection methods based on feature points have exposed many problems, such as undetected points, low detected ratio and cannot well process object occlusion and scaling situation, this paper proposes a detection method which based on a deformable part model. The method uses histogram of oriented gradient (HOG) feature as the object description, and the deformable part model includes a global template and several high-resolution templates. And the method uses the support vector machine (SVM) training the object model. In the learning process, after the HOG feature extracted, the method modifies the HOG feature, and then uses the principal component analysis (PCA) method reducing feature dimensions to avoid over-learning, and improve the detection rate in the detection process. The experiment results shows that the method proposed can better process object occlusion or scaled situation, and there’s also an improvement in detection ratio.

Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles by using monocular infra-red cameras. As some features of vehicles, such as headlight and taillights are more visible at night time, we propose a weighted version of the deformable part model. We define weights for different features in the deformable part model of the vehicle and try to learn the weights through an enormous number of positive and negative samples. Experimental results prove the effectiveness of the algorithm for detecting close and medium range vehicles in urban scenes at night time.

In this paper, we propose the dense disparity map-based pedestrian detection method for intelligent vehicle. The dense disparity map is utilized to improve the pedestrian detection performance. Our method consists of several steps namely, obstacle area detection using road feature information and column detection, pedestrian area detection using dense disparity map-based segmentation, and pedestrian detection using optimal feature. The first step is to detect all obstacle areas using column detection and pedestrian height information. However, there are many objects in single obstacle area. Thus each obstacle area needs to be separated into single object for improving pedestrian detection performance. Thus, the second step is performed to segment the detected obstacle area. The last step is to detect only pedestrian using classifier trained by optimal feature. The optimal feature is extracted by positive and negative training images. ETH database is utilized to evaluate our proposed pedestrian detection method.

We describe an adaptive weighted deformable part model for object detection based on traditional deformable part model(DPM). In the original DPM model, we find that the high response score region calculated by the template filter as high-energy regions, which indicates that the influence on the detection results is greater. The parts can affect the results of object detection, some important parts may directly determine the accuracy of the results, and some unimportant parts even produce bad impacts. To reduce the adverse effects caused by unimportant part filter, we add an adaptive coefficient strategy to the traditional method, which could improve the accuracy of object detection without efficiency loss. The proposed algorithm is better in accuracy compared with the traditional deformable part model, especially in the case of occlusion, with the same efficiency.

In order to solve the problem that the traditional pedestrian recognition and ranging are not satisfactory, a monocular visual nighttime pedestrian recognition and fast ranging method suitable for vehicle infrared night vision images is proposed. Based on the non-contact thermal imaging principle of far-infrared night vision goggles and mathematical morphology method, an improved pedestrian detection and recognition method for three-frame differential monocular vision and geometric motion rate is established. On the basis of pedestrian recognition, monocular camera fast ranging algorithm is used for fast ranging pedestrians. The experimental results show that this method has the characteristics of high pedestrian recognition accuracy and high real-time ranging accuracy.

Object detection is fundamental for realization of self-driving cars and smart traffic management. Various vision based techniques are based on Deformable part models (DPMs) and recently convolutional neural network (CNN) for its anticipated high precision. The DeepPyramid DPM model maps DPM into multiple CNN layers as an assembly of the two concepts with intensive computations. While the recent Region Proposal Network (RPN) allows convolutional layers sharing with further detection steps to accelerate Fast R-CNN. Our framework employs these cost-free RPN proposals and DenseNet with Fast Fourier Transform (FFT) to introduce a faster and more accurate class independent and region based DeepPyramid DPM detector. Our evaluation on four different benchmarks shows that our technique processes PASCAL VOC at 20 fps and VGA images at 14 fps on a GPU (about 12x faster than DeepPyramid DPM with better precision).

We describe a state-of-the-art system for finding objects in cluttered images. Our system is based on deformable models that represent objects using local part templates and geometric constraints on the locations of parts. We reduce object detection to classification with latent variables. The latent variables introduce invariances that make it possible to detect objects with highly variable appearance. We use a generalization of support vector machines to incorporate latent information during training. This has led to a general framework for discriminative training of classifiers with latent variables. Discriminative training benefits from large training datasets. In practice we use an iterative algorithm that alternates between estimating latent values for positive examples and solving a large convex optimization problem. Practical optimization of this large convex problem can be done using active set techniques for adaptive subsampling of the training data.

Nowadays, the areas such as intelligent-assisted driving, intelligent monitoring, pedestrian analysis, and attitude detection are developing rapidly. Inseparable from these fields is pedestrian detection technology. In order to deploy pedestrian detection algorithms on devices with limited hardware resources, a recognition algorithm that takes into account both performance and speed is required. Thus, a Little-YOLOv4 network structure is proposed, where the GhostNet is used to extract image features and the PANet is ameliorated by adding BiFPN path fusion which can integrate richer semantic features and preserve spatial information. The DO-DConv and DSC take place of standard convolution and the ReLU6 replaces the Leaky ReLU, which reduce the computational cost. The squeeze-and-excitation network is added to YOLOv4 head network, which could greatly reduce the interference information. The pedestrian detection results show that the mAP is 90.11% and the FPS is 79 by using Little-YOLOv4. The Little-YOLOv4 network structure could achieve a good compromise between algorithmic accuracy and speed.