Vehicle tracking and analysis within a city

A Hadoop-enabled sensor-oriented information system for knowledge discovery about target-of-interest

“…The mathematical model about the expected behavior of target-of-interest is formulated according to the geographic information and the historical records about target-of-interests. The core calculations corresponding to mathematics modeling include (1) statistical analysis about the historical sensory data stored in Hadoop Distributed File System (HDFS), and (2) numerical solution to the mathematical simulation (e.g., using finite element method [9][14] [16][23] to solve the temporal and spatial-dependent partial differential equations. (4) translation of optical behavior features (i.e., the velocity and position of moving TOIs within the sensor coordinate system) of detected pedestrians into their actual geographical features (i.e., the velocity and position of moving targets within the geographic coordinate system); (5) documentation, which posts the output in a format suitable for post-processing and includes position, velocity, and track.…”

“…In the context of sensor-oriented analysis, data analytics of sensory data aims to disclose specific behavior of target-of-interest (TOI) out of sensory data [9][10] [16]. For example, it is a significant task to detect those speeding or wrong-way vehicle out of surveillance video on the road; therefore a description about the expected (or normal) traffic flow is needed so that those abnormal vehicles can be identified.…”

“…As a result, a SOIS-Hadoop system should be scalable so as to solve the largescale problems. In this work, a macro-cell strategy, which partitions the global physics domain (or scene) into multiple overlapping/non-overlapping element (cell) and then manipulates them independently [9][10] [16][48] [49], will be employed to enhance the capability of the SOIS-Hadoop framework to handle large-scale problems. As illustrated in Fig.…”

“…[1][2][3][4]. Sensor-oriented information systems addressed in this work aim to obtain a panoramic, timely, trusted understanding about the observed behavior of targets-of-interest (TOI) [3] [5][6][7][8][9][10] by exploiting networked sensor assets, which consist of large number of autonomous, heterogeneous, and multi-layer sensor nodes. It is an extremely computationally intense, labor-intensive and highly unreliable job to derive a real-time situational awareness about TOI from high volume, high generation velocity, wide variety of sensory data, the addressed sensor-oriented information system is constructed over Hadoop Ecosystem [8] [11], a conventionally used big-data platform.…”

“…This paper proposes a generic sensor-oriented information system based on Hadoop cluster (SOIS-Hadoop) [11][12][13][14] to monitor and analyze the specific behavior of target-ofinterest (TOI) according to persistent surveillance sensory data. The addressed SOIS-Hadoop has the following features: (1) Employing temporally and spatially dependent mathematical models [3] [9][15] [16] to formulate the expected behavior about targets-ofinterest, based on which the observed behavior of TOI can be evaluated or the future behavior of TOI can be predicted; (2) Tracking TOI through deploying networked autonomous sensor nodes, which will be tuned using collective control and self-optimization to achieve the optimal, reliable, and energy-efficient observations; (3) Using Hadoop Ecosystem to handle the acquisition, fusion, storage, management and mining of large-scale real-time/historical sensory data.…”