Deep learning based pedestrian detection

With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different receptive field scales, we concat the features of different layers with the dilated convolutional features to get the final features. Finally, the candidate boxes are sent to the boosted decision trees to be classified using the scores and features. We evaluated our method on the Caltech Pedestrian Detection Benchmark. Comparing with other state-of-the-art detection methods, the proposed framework with dilated convolution has better performance.

Machine learning (ML) and image processing techniques have been applied together to various scenarios for the development of Intelligent Vehicles. Among these scenarios, pedestrian detection has received growing interest in recent years, since high concern for safety applications in traffic has arisen. Several ML methods were successfully applied to solve this problem. However, because pedestrian detection is in general computationally intensive, a good trade off between accuracy and processing time is desirable, particularly if the methods are directed to real-time applications. Optimum Path Forest (OPF) classifier is a recently developed non-parametric classifier method. This work contribution is the performance assessment of a novel OPF application to pedestrian detection. Results have shown that it is fast and competitive against established methods and a viable alternative to be considered for machine learning and pedestrian detection applications.

In the field of computer vision, pedestrian detection is a significant research area that is frequently applied in areas like intelligent driving and video surveillance. The reliability and accuracy of subsequent tasks like pedestrian tracking and behavior analysis are directly impacted by the accuracy and effectiveness of pedestrian detection. However, there are still a number of difficulties with pedestrian detection tasks because of the variety of factors, including the shape, scale, and posture of pedestrians, as well as background interference and changes in lighting. Based on research on pertinent domestic and international literature, this article examines the key techniques for identifying pedestrians. Introduce frequently used datasets for pedestrian detection first. The model can then be split into two categories based on its training process: one-stage detection algorithms and two-stage detection algorithms. For each stage type, this study offers a thorough introduction to representative algorithms. A summary of its current state of development and future prospects was then given.

Vision-based pedestrian detection technology is widely used in security surveillance, such as transportation, prisons, and important places. Security monitoring requires pedestrian detection not only to be accurate and reliable, but also to meet the needs of real-time monitoring, and the false alarm rate is low. The existing pedestrian learning method based on deep learning has high reliability, but the timeliness cannot meet the monitoring requirements. Traditional pedestrian detection methods are time-sensitive, but false alarm rates tend to be higher. This paper proposes a self-learning pedestrian detection method, combined with motion detection, HOG feature extraction, and SVM classification for preliminary pedestrian detection. The false alarms caused during the pedestrian detection process are manually confirmed, and then the system automatically detects the false alarm image at a specific time. Self-learning, retraining the classifier, so as to continuously reduce the pedestrian detection false alarm rate and improve the pedestrian detection performance index through self-learning during the system operation, and the timeliness can meet the needs of video surveillance. Experiments show that the false alarm rate of this method is low, timeliness is high, and the recognition rate is also high.

Facing the problems of low working efficiency, poor accuracy and insufficient stability of traditional motion detection algorithms and machine learning algorithms in pedestrian detection, this paper will take artificial intelligence technology as the core, adopt R-CNN series deep learning models, combine Support Vector Machine (SVM) classifier, and rely on TensorFlow deep learning framework to realize pedestrian detection and recognition in video and picture files with the support of OpengCV Open Source Visual Library. Combined with ASP.NET framework, C# language is used to complete the design and development of each functional module, improve the deployment of the corresponding API interface, and build a front-end interactive interface to form a pedestrian detection system based on Web. The overall design of the system chooses B/S architecture, which allows users to access the Web Server through simple request operation to complete the detection of video image data in the database and return the detection results to the front page for display. The system will greatly improve the precision and accuracy of pedestrian detection and make a positive attempt to further expand the application scenarios of pedestrian detection in video images.

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.

Pedestrian detection is another significant special application of object detection in autonomous vehicles. In contrast to universal object detection, it has similarities and special traits. Nevertheless, there are some difficulties that influence pedestrian detection performance, namely (i) occlusion and deformation (ii) low-quality and multispectral images consisting primarily of lighting conditions, small-scale detection, and target detection extensively, and (iii) true-false pedestrians. Deep learning (DL) methods are a class of artificial intelligence method that can solve the issues mentioned above of pedestrian detection. This paper initially gives an elaborate description of pedestrian detection, difficulties in pedestrian detection, and latest advancements in solving them using the assistance of DL methods with informative discussions, aiming to provide insights to the readers. (2) A new pedestrian detection algorithm (PDA) of true/false pedestrian is suggested here, in which a new YOLO-3D CNN model is applied to reject true/false pedestrian. The primary purpose is to evaluate the performance of the existing 3D CNN taking into consideration the problem of rejecting true false pedestrians based on images captured using the car's onboard cameras and light detection and ranging (LiDAR) sensors. PDA initially utilizes YOLOv3 to capture the entire image for training detector model capable of real-time forecasting. Next, as a feature extractor, it utilizes the MobileNet-SSD that provides great accuracy as well as good trading uptime. PDA then implements the Faster R-CNN method to detect different parts of the object, over the convolutional layer. Lastly, data augmentation techniques are applied in PDA to augment the data coverage by fully exploiting available training data. Simulation results indicate that the proposed pedestrian detection model and PDA improve the accuracy of real and false pedestrians while maintaining real-time requirements.

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.

While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providing robustness against partial occlusions to the whole body. We also propose an Adaptive Joint Non-Maximum Suppression (AJ-NMS) to replace the original NMS algorithm widely used in object detection, leading to higher precision and recall for detecting overlapped pedestrians. Experimental results on the public-domain CUHK-SYSU Person Search Dataset show that the proposed approach outperforms the state-of-the-art methods for joint pedestrian and body part detection in the wild.

The goal of this study is to clarify the usefulness of deep learning methods for pedestrian collision detection using dashcam videos for advanced automatic collision notification, focusing on pedestrians, as they make up the highest number of traffic fatalities in Japan. First, we created a dataset for deep learning from dashcam videos. A total of 78 dashcam videos of pedestrian-to-automobile accidents were collected from a video hosting website and from the Japan Automobile Research Institute (JARI). Individual frames were selected from the video data amounting to a total of 1,212 still images, which were added to our dataset with class and location information. This dataset was then divided to create training, validation, and test datasets. Next, deep learning was performed based on the training dataset to learn the features of pedestrian collision images, which are images that capture pedestrian behavior at the time of the collision. Pedestrian collision detection performance of the trained model was evaluated as the percentage of correct predictions of pedestrian collisions in image data according to varied test sets with different combinations of characteristics. Our results for the proposed method show high-precision collision detection for daytime, clear pedestrian wrap trajectory accident data, including accurate detection of pedestrian collision location information. However, nighttime, unclear accident data resulted in false detection or no detection. Reduction of exposure value and resolution was confirmed to reduce detection rate. The results of the present study suggest the possibility of pedestrian collision detection by deep learning using dashcam videos.

This article presents the improvement of an Inverse Perspective Mapping (IPM) based obstacle detection algorithm and its utilization in a pedestrian candidate detection module of our vision based pedestrian detection system. A vertical 1D profile of the IPM detection on the region of interest termed Pedestrian Detection Strip (PDS) is created first and the candidates are chosen by applying a threshold on the profile. The usage of the vertical profile increases the robustness of the detection on low contrast images as well as distant pedestrians significantly. A low level pedestrian oriented segmentation and fast symmetry search on the leg region of pedestrians is also presented.

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

Gait recognition is a noncontact biometric procedure that determines the identity or health status of a person by analyzing his or her walking posture and habits, including skeletal and joint movements. The most remarkable feature of this method is the possibility of conducting recognition without demanding much cooperation from participants. Therefore, this recognition technique has attracted much attention from scholars. Additionally, because of the rapid development of graphics processing unit technology, related hardware and computation performance, the applications of deep-learning technology are considerably enhanced. The objective of this study was to apply a deep neural network (DNN), which employs deep-learning technology, to achieve gait-based automatic pedestrian detection and recognition. In contrast to using wearable devices to precisely capture skeletal and joint movements, pedestrian color-image sequences were used as input in this study. Subsequently, a pretraining convolutional neural network (CNN) was employed to capture pedestrian location and extract pedestrian dense optical flow to serve as concrete low-level feature inputs. Then, a finely-tuned DNN based on the wide residual network was employed to extract high-level abstract features. In addition, to overcome the difficulty of obtaining local temporal features by using a 2D CNN, part of the 3D convolutional structure was introduced into the CNN. This design enabled use of limited memory to acquire more effective features and enhance the DNN performance. The experimental results show that the proposed method has exceptional performance for pedestrian detection and recognition.

In recent years, autonomous vehicles have become more and more popular due to their broad influence over society, as they increase passenger safety and convenience, lower fuel consumption, reduce traffic blockage and accidents, save costs, and enhance reliability. However, autonomous vehicles suffer from some functionality errors which need to be minimized before they are completely deployed onto main roads. Pedestrian detection is one of the most considerable tasks (functionality errors) in autonomous vehicles to prevent accidents. However, accurate pedestrian detection is a very challenging task due to the following issues: (i) occlusion and deformation and (ii) low-quality and multi-spectral images. Recently, deep learning (DL) technologies have exhibited great potential for addressing the aforementioned pedestrian detection issues in autonomous vehicles. This survey paper provides an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques. Informative discussions and future research works are also presented, with the aim of offering insights to the readers and motivating new research directions.

This paper presents a project on a driver assist system which uses the concept of computer vision and a deep learning to detect and identify any pedestrians who suddenly step into oncoming traffic so as to provide an alert or warning to the driver of the vehicle in case of a collision. Since pedestrian-related accidents are increasing every year, with the development of artificial intelligence, machine learning, and deep learning-based pedestrian detection methods has greatly improved the accuracy of pedestrian detection and object tracking becomes much easier. In this project, a deep learning algorithm known as the YOLO algorithm which uses a joint training algorithm is used which helps in the detection and classification of objects. Therefore, with the use of both cameras and the concept of artificial intelligence an efficient pedestrian scanning and identification method can be developed.