Pedestrian Stride Frequency and Length Estimation in Outdoor Urban Environments using Video Sensors

Amid concerns for the environment and public health, there has been recently a renewed emphasis on active modes of transportation, i.e. walking and cycling. However, these modes have traditionally received research and practice focus secondary to motorized modes. There is consequently a lack of pedestrian data, in particular microscopic data, to meet the analysis and modeling needs. For instance, accurate data on individual stride length is not available in the transportation literature. This paper proposes a simple method to extract automatically pedestrian stride frequency and length from video data collected non-intrusively in outdoor urban environments. Pedestrian walking speed oscillates during each stride, which can be identified through the frequency analysis of the speed signal. The method is validated on real world data collected in Rouen, France, and Vancouver, Canada: the root mean square errors on stride length are respectively 6.1 and 5.7 cm. A method is proposed to distinguish pedestrians from motorized vehicles and used to analyze the 50 min of the Rouen dataset to provide the distributions of stride frequency and length. Saunier, El Husseini, Ismail, Morency, Auberlet and Sayed 4 INTRODUCTION Walking is a key non-motorized mode of travel and a vital component of most trips. Many road agencies have developed programs aimed to reduce traffic emissions by promoting less polluting forms of transportation such as walking and cycling. Public health researchers (agencies) also see in non-motorized modes of travel an opportunity to increase the level of physical activity among population at risk. Developing a better understanding of pedestrian movement is vital for improving the design methods of non-motorized and sustainable modes of travel. Such understanding can also increase the accuracy of behavioral models trying to link physical activity, health problems such as obesity, neighborhood design and travel behaviors. As well, pedestrians sustain the highest share of fatal road collisions among nonmotorized modes of travel. Therefore, non-motorized modes of travel are receiving more emphasis in transportation engineering as the public and policy makers become more aware of issues of urban sustainability and energy supply, and health concerns regarding the lack of physical activity of the population. However, these modes of travel, and walking in particular, have traditionally received research and practice focus secondary to motorized modes. The consequence is that there is a lack of pedestrian data, in particular microscopic data, to meet the analysis and modeling needs. For instance, accurate data on individual stride length are not available in the transportation literature. A previous project aiming to estimate the potential gain in physical activity for individual switching from a motorized mode to walking for short trips relied on simple estimations of stride length to convert distances into steps (1) (2). General hypothesis also had to be formulated regarding the average speed of walking and inherent energy expenditure. There is little research that discusses pedestrian stride length and the validity of currently used values. Pedestrian data can be collected with various levels of automation. As manual data collection is expensive, time-consuming and error-prone, there are ongoing efforts to develop automated video-based methods. A video analysis system was developed to detect and track automatically all moving objects for road safety analysis (3) (4). It can also detect and track pedestrians and was successfully applied to the collection of walking speed data (5) and the study of pedestrian-vehicle interactions for safety analysis (6) (7). This work presents a further use of pedestrian microscopic data, i.e. trajectories. Gait is the pattern of movement of the limbs of animals, including humans. This work deals with pedestrian walking gait, which is usually described by the following walking parameters: the walking velocity v, the stride frequency f and the stride length l, which are related by the following relationship . During the preparation of (5), the display of pedestrian walking speeds extracted from video data showed that speed fluctuates periodically at each stride (see FIGURE 1 and FIGURE 2f,k): identifying the strides becomes thus possible and permits to measure stride frequency and length. While there has been much research on pedestrian walking speed, stride length is not so commonly measured, even less automatically nonintrusively in the field. Distributions based on empirical measures are crucial for studies trying to estimate the impact of a shift from motorized modes to active transportation on the level of physical activity. Commonly, simple average estimations are used to translate distances into steps and energy expenditure (1) (2). This is a very crude model of the reality. Producing distributions of stride length and walking speeds, according to various conditions or population attributes would greatly enhance the current estimation methods. The current paper establishes the basis for such contributions. It presents an automated method for the estimation of stride frequency and length from trajectory data extracted from video data. The background of this work is presented in the next section. It is followed by a description of the proposed methodology, which is then validated on a new video dataset collected at an urban intersection in Rouen, France. Finally the paper is concluded and future work is discussed. Saunier, El Husseini, Ismail, Morency, Auberlet and Sayed 5