Automated Collection of Pedestrian Data Using Computer Vision Techniques

Pedestrian data collection is critical for the planning and design of pedestrian facilities. Most pedestrian data collection efforts involve field observations or observer-based video analysis. These manual observations are time consuming, limited in coverage, resource intensive and error prone. Automated video analysis which involves the use of computer vision techniques can overcome many of these shortcomings. Despite advances in the field of computer vision applications for pedestrian detection and tracking, the technical literature shows little use of these techniques in pedestrian data collection practices. The likely reasons are the technical complexities that surround the processing of pedestrian videos. To extract pedestrian trajectories automatically from video, all road users must be detected, tracked at each frame and classified by type, at least as pedestrians and non-pedestrians. This is a challenging task in busy open outdoor urban environment. Common problems include global illumination variations, multiple object tracking and shadow handling. Specific problems arise when dealing with pedestrians because of their complex movement dynamics, varied appearance and non-rigid nature. The main objective of this study is to present a system for automated collection of pedestrian walking speed using computer vision techniques. The system is based on a previously developed feature-based tracking system for vehicles which was significantly modified to adapt to the particularities of pedestrian movement and to discriminate pedestrian and motorized traffic. The system was tested on real video data collected at Downtown area of Vancouver, British Columbia. This study is unique in so far as it tests the system under a variety of daylight conditions, crowd densities, movement context, and the video analysis approach. Promising results were obtained and several conclusions were drawn using statistical analysis of the automatically extracted pedestrian trajectories. Ismail, Sayed, and Saunier 2 INTRODUCTION Walking is the most basic means of traveling and is a main driver for a sustainable, healthy, clean, resource-efficient and livable urban environment. Therefore, new urban planning concepts have been redefining the function and mode-assignment of streets by emphasizing walkability as well as changing industry standards and professional practice in order to accommodate the pedestrian as a key road user (1). The emergence of the pedestrian as a key road user in an urban environment is an element in a larger theme that concerns the creation of a more sustainable transportation system. The revival of the theme is likely a public response to global changes in energy resources as well as a desire for improving the quality of life in urban areas. Despite findings in the literature that corroborate the importance of non-motorized traffic and in particular pedestrians, these modes of transportation are in general overlooked, and understudied relative to vehicular traffic. For example, current trip counts capture 16-33% of actual non-motorized trips (2), while collecting reliable non-motorized traffic information remains challenging (3). Planning for pedestrian facilities and modeling of pedestrian demand are areas of research that are yet to be developed to a level that matches vehicular traffic (4). Real data is critical for the development and calibration of design and planning models for pedestrian facilities. Many design applications involve individual (microscopic) observations of pedestrian movement. For example, microscopic observational data is required to investigate the ability of individual pedestrians to vary their walking speed based on a signal indication, potential conflict with motorized traffic (5) or in response to external stimuli (6). In addition, microscopic pedestrian observations can provide valuable insight for pedestrian modeling, e.g. inter-person spacing and pedestrian maneuvering (7) and obstacle navigation (8). Although at a relatively advanced stage in theory and analysis, pedestrian simulation models are generally based on limited understanding of microscopic pedestrian behavior (8) and limited validity that stems from real data (7) (9). Collecting observational data for pedestrians is particularly challenging due to the less organized nature of pedestrian traffic compared to vehicular traffic (10). The main methods are: manual field observations, manual observations from videos, semi-automated video analysis, and automated video analysis. Manual field observation, which is the common method of pedestrian data collection, is in general more expensive, error-prone, and time consuming compared to video analysis (11). Generally, the use of video sensors has several advantages. First, it captures naturalistic pedestrian movement with limited risk of stirring the attention of observed subjects, who may behave unnaturally if felt being watched (12). Other advantages include the relative ease of installation, the richness of the data that can be extracted (i.e. complete trajectories), the large area that can be covered and their low cost. However, manual video observations are time consuming, resource intensive, and error-prone. Semi-automated analysis, or time-lapse analysis, of pedestrian movement involve the use of image processing tools to manually mark or track pedestrians in a sequence of video images, e.g. (13) (14). Manual operations in semi-automated video analysis are laborious and limited in terms of data volume that can be analyzed compared to automated methods. Automated video analysis which involves the use of computer vision techniques can overcome many of the shortcomings associated with manual field observations and manual video analysis. Ismail, Sayed, and Saunier 3 The transportation literature contains few studies that involved applying computer vision techniques to collect pedestrian data in real settings, especially in busy “open” outdoor urban environment, such as areas around an intersection and transit hubs. Open environment refers to the mixed traffic, including motorized vehicles and pedestrians, the variable environment, the multiple flows of moving objects that may enter and leave the scene, and stop for varying amounts of time in the field of view. Automated pedestrian data collection in such environments remains a largely unsolved problem in the field of computer vision. Most published work is limited to idealized conditions using small datasets. The primary objective of this study is to document the development and testing of a prototype system that is capable of extracting real-world pedestrian tracks from a video taken at traffic intersections. The study is unique in regard to the developed video analysis technique as well as in testing the developed system under different conditions of lighting, crowdedness, and traffic mix in an open and uncontrolled environment. The paper discusses the technical issues that arose during the system development are described along with techniques for resolving these difficulties. The walking speed automatically calculated by the system was validated in comparison to walking speeds extracted by human observers. The system accuracy in automatically measuring pedestrian speed was satisfactory and provided support and reliability for analysis results. A case study is introduced using video data collected for pedestrian movement in a main commercial corridor in the Downtown area of Vancouver, British Columbia. The case study was validated and demonstrated satisfactory accuracy of the system. The paper includes a statistical analysis of the case study results and reports the findings. The next section reports a review of previous work on the subjects of pedestrian walking speed and pedestrian detection and tracking, followed by a description of the developed system for automatically collecting pedestrian walking speed data from video sequences. Following sections report the data collection effort, system testing, and validation results. The paper concludes with statistical analysis of the walking speed data obtained from the testing datasets and a summary of conclusions drawn from the entire study.