Bicycle Tracking Research Articles

An electric bicycle tracking algorithm for improved traffic management

This paper proposes an efficient electric bicycle tracking algorithm, EBTrack, utilizing the high-precision and lightweight YOLOv7 as the target detector to enhance the efficiency of illegal detection and recognition of electric bicycles. The EBTrack effectively captures the position and trajectory of electric bicycles in complex traffic monitoring scenarios. Firstly, we introduce the feature extraction network, ResNetEB, specifically designed for feature re-identification of electric bicycles. To maintain real-time performance, feature extraction is performed only when generating new object IDs, minimizing the impact on processing speed. Secondly, for accurate target trajectory prediction, we incorporate an adaptive modulated noise scale Kalman filter. Additionally, considering the uncertainty of electric bicycle entry directions in traffic monitoring scenarios, we design a specialized matching mechanism to reduce frequent ID switching. Finally, to validate the algorithm's effectiveness, we have collected diverse video image data of electric bicycle and urban road traffic in Hefei, Anhui Province, encompassing different perspectives, time periods, and weather conditions. We have trained the proposed detector and have evaluated its tracking performance on this comprehensive dataset. Experimental results demonstrate that EBTrack achieves impressive accuracy, with 89.8 % MOTA (Multiple Object Tracking Accuracy) and 94.2 % IDF1 (ID F1-Score). Furthermore, the algorithm effectively reduces ID switching, significantly improving tracking stability and continuity.

PENDAMPINGAN DAN PEMBERDAYAAN PENGELOLAAN KEUANGAN PADA LPD DESA ADAT CEPAKA

Covid 19 pandemic has a huge impact on the Cepaka Village Community, starting from the closure of the tourism sector for foreign tourists, SMEs with low orders, culinary selling products with a low cost strategy, jogging tracks and bicycle tracking which are crowded with domestic tourists, cannot provide economic benefits, so does not give value to farmers who own rice fields that are traversed by cycling tourists or tourists who jog and take selfies as well as the financial behavior of rural communities that tends to be consumptive, causing the Cepaka Village community to not be economically prosperous. The focus of this activity is carried out based on this grouping, namely Creating, packaging / packaging tourism villages by involving all existing potentials, and involving the village community. Management / governance of UKM and services that will be the core products of tourism villages and the impact of tourism villages, and Carry out assistance activities for community financial institutions in cooperatives, LPDs, traditional markets, waste banks and BUMDES of Cepaka Village.

SLFNs interpolation fingerprint particle filter-based shared bicycle tracking algorithm

In order to improve the performance of traditional fingerprint detection method in the process of tracking the shared bicycle, the inertial sensor is used for data measurement. The particle filter (PF) method is a widely used sensor fusion algorithm, but the initialisation and weighting processes are problematic in shared bicycle positioning systems. In this paper, a new PF scheme is proposed, and it can produces smooth and stable localised knowledge. However, the feed-forward network that uses the single hidden layer is used to simulate the estimation and improvement of the performance of multiple probability to achieve the distinction of similar fingerprints. At the same time, the random sample consensus algorithm (RANSAC) is used to initialise the algorithm so as to reduce the convergence time. Experiments show that the tracking error of this scheme is less than 1.2 m, which is superior to the selected comparison method.

SLFNs interpolation fingerprint particle filter-based shared bicycle tracking algorithm

SLFNs interpolation fingerprint particle filter-based shared bicycle tracking algorithm

The usefulness of GPS bicycle tracking data for evaluating the impact of infrastructure change on cycling behaviour.

Issue addressed A key strategy to increase active travel is the construction of bicycle infrastructure. Tools to evaluate this strategy are limited. This study assessed the usefulness of a smartphone GPS tracking system for evaluating the impact of this strategy on cycling behaviour. Methods Cycling usage data were collected from Queenslanders who used a GPS tracking app on their smartphone from 2013-2014. 'Heat' and volume maps of the data were reviewed, and GPS bicycle counts were compared with surveillance data and bicycle counts from automatic traffic-monitoring devices. Results Heat maps broadly indicated that changes in cycling occurred near infrastructure improvements. Volume maps provided changes in counts of cyclists due to these improvements although errors were noted in geographic information system (GIS) geo-coding of some GPS data. Large variations were evident in the number of cyclists using the app in different locations. These variations limited the usefulness of GPS data for assessing differences in cycling across locations. Conclusion Smartphone GPS data are useful in evaluating the impact of improved bicycle infrastructure in one location. Using GPS data to evaluate differential changes in cycling across multiple locations is problematic when there is insufficient traffic-monitoring devices available to triangulate GPS data with bicycle traffic count data. So what? The use of smartphone GPS data with other data sources is recommended for assessing how infrastructure improvements influence cycling behaviour.

Real-Time System for Tracking and Classification of Pedestrians and Bicycles

Data on pedestrian and bicycle volumes are necessary for transportation planning, infrastructure design, and traffic management. Nevertheless, such data cannot be collected directly by the commonly used detectors (e.g., inductive loops, sonar, and microwaves). In this study, a pedestrian and bicycle tracking and classification system was developed to detect pedestrians and bicycles with a video camera. This system contained six modules: a video flow capture module, a movement detection module, a shadow removal module, a feature extraction module, a tracking module, and a classification module. The Gaussian mixture model was used to extract moving objects from an image sequence. In the tracking module, the most challenging part of this system, the trajectories were obtained by use of a Kalman filter. To identify pedestrians and bicycles, a backpropagation neural network was used in the classification module. Two other simple but effective algorithms were used to alleviate the negative impacts of shadows and occlusions. The system was tested at three sites under different traffic and environmental conditions. It has been confirmed that the accuracy for pedestrian detection was approximately 85% and the count error rate was less than 13% for bicycles at all test sites. The proposed system is a feasible alternative for the collection of data for nonmotorized travel modes.