Abstract
Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms.
Highlights
The problem of detection and recognition of moving objects in deep learning lies in detecting the location of the object and segmentation with removing its background [1]
The last process is to classify the moving object using the hybrid technique of Kalman filtering followed by convolutional neural network (CNN), which achieved accuracy of 100%
Two basic steps are applied, the first step is for the detection and tracking of the object removing background by Kalman filtering, the second step is applying the CNN to recognize the object
Summary
The problem of detection and recognition of moving objects in deep learning lies in detecting the location of the object and segmentation with removing its background [1]. The recognition model requires object‟s image without background and a correct categorical label that enables the model to predict the correct location and label the moving object [2]. It is very important to organize knowledge at various levels, and this issue has taken a great interest in Cognitive Psychology, for example in Brown's work, a cat cannot only be thought of as a cat, but a quadruped, boxer, or in general an animated being. This paper is organized as follows: Section 2 deals with related work, while the Section 3 focuses on special details required for theoretical background.
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