The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system. SenseFlow utilises off-the-shelf devices which sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in city to evaluate the scalability and performance "in the wild", and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.
Apart from cameras, other devices used for people tracking are range finders, such as radar, and sonar. @cite_10 , proposes people tracking by using multiple layers of 2D laser range scans. @cite_19 presents a valuable analysis of pedestrian detection in urban scenario using exclusively lidar-based features. Unfortunately, the impressionable wave and laser signal lead a large number of false negatives @cite_4 .
@cite_19 @cite_10 @cite_4
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Reliable detection and classification of vulnerable road users constitute a critical issue on safety protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and or Radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection in urban scenario using exclusively LIDAR-based features. The aim is to explore how much information can be extracted from LIDAR sensors for pedestrian detection. Moreover, this study will be useful to compose multi-sensor based pedestrian detection systems using not only LIDAR but also vision sensors. Experimental results using our data set and a detailed classification performance analysis are presented, with comparisons among various classification techniques. People detection is a key capacity for robotics systems that have to interact with humans. This paper addresses the problem of detecting people using multiple layers of 2D laser range scans. Each layer contains a classifier able to detect a particular body part such as a head, an upper body or a leg. These classifiers are learned using a supervised approach based on AdaBoost. The final person detector is composed of a probabilistic combination of the outputs from the different classifiers. Experimental results with real data demonstrate the effectiveness of our approach to detect persons in indoor environments and its ability to deal with occlusions.