Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.<br /

Pengembangan Mekanisme Change Detection Untuk Efisiensi Energi Pada Wifi-Based Indoor Positioning System

Pengembangan mekanisme change detection mempunyai peranan penting terhadap Indoor Positioning System (IPS). Namun permasalahan yang masih umum dijumpai adalah konsumsi energi yang tinggi, karena proses WiFi scanning berjalan secara terus menerus. Proses WiFi scanning mengirimkan data dari klien ke server secara terus menerus, terkadang memberikan informasi yang sama dan berulang kepada user. Informasi yang dikirim secara redudan bisa berdampak pada konsumsi energi yang tinggi. Penelitian ini mengusulkan mekanisme perbaikan dengan change detection untuk penghematan energi dalam melakukan sampling secara adaptif pada kekuatan sinyal WiFi dengan accelerometer sebagai trigger. Mekanisme change detection yang dilakukan adalah mengukur nilai pada accelerometer dengan menentukan silent zone. Silent Zone merupakan rentang nilai yang didapatkan ketika accelerometer dalam kondisi diam. Apabila diketahui nilai kekuatan sinyal pada accelerometer melebihi nilai silent zone, maka diidentifikasi user dalam kondisi bergerak dan secara otomatis proses WiFi scanning akan berjalan. Change detection dengan Bluetooth mempunyai proses yang sama dengan menggunakan accelerometer. Algoritma yang diusulkan dapat menghasilkan penghematan daya baterai sebesar 4,384% untuk scanning dengan change detection menggunakan accelerometer dan 2,666% untuk change detection menggunakan Bluetooth. ================================================================================================================================ The development of change detection mechanism has an important role in the Indoor Positioning System (IPS). In IPS technology, a lot of battery power will be used because the WiFi scanning process runs continuously. The WiFi scanning process sends data from the client to the server continuously, sometimes providing the same and repeatable information to the user. Information sent redundantly can have an impact on high energy consumption. In this research, the researchers developed a repair mechanism with change detection to save energy in an adaptive sampling of the strength of the WiFi signal with the accelerometer as a trigger for the adaptive process. Change detection mechanism that is done is measuring the signal strength on the accelerometer by determining the silent zone. Silent Zone is the range of values obtained when the accelerometer is at rest. If it is known that the signal strength value on the Accelerometer exceeds the value of the silent zone, the user is identified in a mobile condition, and the WiFi scanning process will automatically run. Change detection with Bluetooth has the same process using an accelerometer. The algorithm we propose can produce a battery-saving of 4.384% for scanning with change detection using an accelerometer and 2.666% for change detection using Bluetooth