Rubaiya Hafiz, Senior Lecturer Department of Computer Science and Engineering, Daffodil International University, Dhaka. Deep knowledge and great interest of our leader in the field of "Object Detection" to realize this thesis. We would like to thank all our course fellow at Daffodil International University who participated in this discussion during the completion of the course work.
The proposed method is applicable to indoor and outdoor environment to visualize the object for blind people. This is an important technique in the field of object detection to detect each object by processing video images and segmenting them using background subtraction. Segmentation is very important for detecting any feature from any object that is moving.
The image detection depends on the c image, so we try to make all the images the same size because the sensors detect the same size image. There are millions of people in this world who are unable to understand the environment. In our system, we tried to detect the object, so blind people are aware of everything like normal people.
It is very difficult to understand what they have on the front, so we are working to develop a system for the blind that would help them in several ways.
Motivation 1-2
Report Layout 3-4
In this chapter, we have added the general process we used to build this system and all the methods are interpreted step by step, as well as the essential figures and diagrams used to complete our proposed method. In this section, we looked at the conclusions and future work of our proposed system and how we can improve the system for the blind. 1] they used the Kalman filter based on the centroid of the moving object part in the minimal bounding box.
This method cannot provide direction or additional information about the object the system has detected. To perform accurate object detection and tracking, their BBSE will create three possible detections for the same object and will rely on the CNN detection. The moving object can be determined by taking the difference between the background image and the input image.
This method is especially efficient for indoor buildings such as schools, shopping malls, museums, hospitals and airports that have regular lines. Their simulation result shows that the implementation method gives a remarkable result in changing the lighting conditions to detect the moving object in the background pattern. In this paper, principal component analysis (PCA) was used to reduce the dimensionality of the HIS data.
Grayscale projection based moving object detection by analyzing the grayscale projection curve in comparative difference subimage. In this work, our main concern was about time management to complete this project on time. In this case, it was a bit challenging for us to increase the communication to our supervisor.
In our last chapter, we reviewed and discussed some algorithms and methods established for object detection. Our system is not only an object detection system, but it is also developed to detect all moving objects. In this chapter we will discuss about our method, what we need to develop.
Methodology 10-14
YOLO is the best of any other algorithm in object detection and real-time object detection. On the other hand, the accuracy rate of object detection from the video dataset is high compared to other algorithms. From the table above, we can say that YOLOv3 is the best in object detection.
The number of times the system detects an object, the system will tone once for each object detection. In the previous chapter, we discussed the related parts of object detection, our methodology that works to detect moving objects. For this implementation we use the YOLOv3 model because it is the best model so far for detecting multiple objects by their type and NumPy for adding multidimensional arrays and matrices and OpenCV for the desired real-time computer vision.
34;Moving Object Detection and Tracking from Video Captured by a Moving Camera." Journal of Visual Communication and Image Representation. 34;Video Image Processing for Moving Object Detection and Segmentation Using Background Subtraction. First International Conference on Computer Systems and of Communications (ICCSC).34. ;An Appearance and Motion-Based Deep Learning Architecture for Moving Object Detection in a Moving Camera IEEE International Conference on Image Processing (ICIP).
34; Moving object detection against sudden illumination change using improved background modeling International Conference on Electrical, Computer and Communication Engineering (ECCE). 34;Video image stitching based on moving object detection and motion prediction compensation rd International Congress on Image and Signal Processing. There are two types of object detection in our project, first we classify the object and the other one is detect object in this work we use python.
We have tried to create a fast and efficient object detection system that helps all blind people to live their lives beautifully like normal people. From the following comparison table we can make a decision about the best method: TABLE (3.5.1): COMPARISON TABLE From the above table we can say that the YOLOv3 is the best in object detection. For this implementation we use the YOLOv3 model because it is the best model yet for detection of multiple objects with their types and NumPy for adding multidimensional arrays and matrices and OpenCV for the desired real-time computer vision.
Object name Object detection accuracy (%) Bicycle 1.00 Car 0.95 Person 0.98 Motorcycle 1.00 Backpack 0.95 Hand bag 0.89 In the above table we show some selected objects and the degree of their detection accuracy in the external and internal environment.
