A Real Time Visual Monitoring Module for Traffic Conditions Based on a Modified Auto-Associative Memory
| dc.contributor.author | Abdul Kareem, Emad Issa | |
| dc.date.accessioned | 2018-07-19T01:59:32Z | |
| dc.date.available | 2018-07-19T01:59:32Z | |
| dc.date.issued | 2012-04 | |
| dc.description.abstract | A new trend of traffic light monitoring module is the module that uses real time visual data and a computer vision approach to reflect the traffic conditions (crowded, normal and empty). This approach determines the traffic conditions by counting the number of vehicles individually on the street with the use of complex techniques. However this gives rise to some limitations. These limitations can be tackled when a multitude of vehicles in the street is detected as a group rather than individually. Such a technique can be achieved by using the auto-associative memory. This research proposes a new monitoring module using the Hopfield associative memory, which is further modified and developed to be able to work with the real time visual data. The newly modified Hopfield network is called the Multi-Connect Architecture (MCA). The evaluations have been directed into three directions: among these three directions, the MCA evaluation has proved to be more efficient than the Hopfield networks. This efficiency is measured in terms of its small network size, small weight size and its large network capacity. Furthermore, common problems, such as global minimum problem, correlation problem, allowable percentage noise and inverse pattern’s value convergence problem in the Hopfield network have been prevented. In addition, the Big-O analysis for MCA showed a lower degree of complexity as compared to the Hopfield approach and hence its ability to work in real time. A second evaluation is conducted to evaluate the module in term of its accuracy and the number of training images needed. Meanwhile, the third evaluation evaluates the monitoring module in real environment. The evaluations have shown that it is possible to determine all the different traffic conditions accurately. The value of accuracy ranged approximately between the average of 90.8% to 100% despite the differences in streets, daytime and weather conditions; thus proving its stability. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/5964 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Traffic light monitoring module is the module | en_US |
| dc.subject | time visual data and a computer vision approach | en_US |
| dc.title | A Real Time Visual Monitoring Module for Traffic Conditions Based on a Modified Auto-Associative Memory | en_US |
| dc.type | Thesis | en_US |
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