Real Time Video Processing and Object Detection on Android

Real Time Video process and intentive lens perception on humanoidReal Time Video bear on and goal contracting on humanoid SmartphoneAbstract As Smartphone is getting to a greater extent potent, can do more superior stuffs that previous required a calculator. For employing the steep touch power of Smartphone is mobile computer day-dream, the ability for a device to capture process analyze understanding of images. For mobile computer vision, Smartphone must be faster and real clock age. In this study ii activitys have been developed on mechanical man platform exploitation fan outCV and content library called as CamTest with own implemented algorithmic programic programs. Efficiency of two Android uses have been comp bed and found that OpenCV performs faster than CamTest. The results of examining the vanquish design detective work algorithm with reverence to efficacy shows that steadfast algorithm has the finest blend of speed and intent spying performance. succeeding(a) projected butt comprehension system utilise flying algorithm, which uses SVM, BPNN for training and validation of mark in real prison term. The practical performance detects the object perfectly with recognition time around 2 ms utilise SVM and 1 ms using BPNN.KeywordsAndroid Video bear upon object detection SVM FAST recess detector BPNNI. INTRODUCTIONAs Smartphone is the perfect combination of personal digital assistant, media player, camera and several other(a) stuffs. It has entirely changed the past about mobile phone. In the early geezerhood of Smartphone application development only mobile company was able to develop. after the introduction of Android OS in 2007, Smartphone application development is high in demand. Android was developed by Google with Linux core kernel and gnu softw atomic number 18 stuffs. 16.The introduction of Smartphone with camera Real Time video impact becomes very trendy now and having most critical computation tasks. just a bout all Smartphone applications uses a camera to use mobile computer vision technology 2. Mobile computer vision technologies playing vital affair in developing our day to day activities applications 1.This technology having many objectives handle object finding, segmenting, stead recognition 2.As Smartphone processors such as MediaTek, ARM, NVIDIA Tegra, and Snapdragon be achieving more computation capability showing a fast ontogeny of mobile computer vision applications, the standardiseds of image editing, augmented reality, object recognition. Long affect time due to the high computational encumbrance averts mobile computer vision algorithms from being practically used in mobile phone applications. To overcome this problem, researchers and developers have explored the libraries such as OpenGL and OpenCV 2. cover developers will face lot problems as he does not having rudimentary idea to process real time video. OpenCV library is the solution which is write in C, C++ l anguage, reduces the complexity for development and research 17 2.Real-Time recognition and detection of objects is complex and favorite area for research in the instantlys fast growing mobile computer vision technology. Applications like political machine vision, visual surveillance robot navigation are the outflank examples 4.Object detection and recognition dwell of three steps raw materialally, first is the sign call downion, second classification and third is the recognition of object using machine training and several other technologies 3.Due to the growth of Scale unceasing Feature Transform (SIFT)10, the object detection method acting using unified filter changed to key show up matching based object detection method 8 10.SIFT is more focusing on invariant key point matching. On the similar concept new algorithms were born such as the Speeded-Up Feature Transform (SuRF)11,Center eludeed Extrema (CenSurE)22, Good Features to Track (GFTT)26, maximally-Stable Extrema l Region extractor (MSER)24, and Oriented Binary productive Independent Elementary Features (ORB)21, and Features from Accelerated member Test (FAST)12 4 6 8.In this paper, real time video processing efficiency was find using OpenCV 17 and CamTest with sponsor of core library. Next analyze best object detection algorithm with see to efficiency in support with OpenCV library. Projected real time object recognition system using FAST algorithm 12, SVM 15 and BPNN 25. All the stuffs have been conducted on LG Optimus Vu Smartphone with Android 4.0.4 OS.II. humanoid ARCHITECTUREThe Android operating system is like other Smartphone OS, with epicurean structure 216. Android operating system stack consist on several layers such Kernel Layer, clay Libraries,Dalvik realistic Machine layer (i.e Android Runtime layer),Application rollwork layer and on top Applications layer 216.The Kernel gives basic funtionalities like network management memory management, process management, device ma nagement. Libraries are used for different oprations like internet security 216.Android Runtime consist of Dalvik Virtual Machine which is optimized for Android and provides core libraries.The Application Framework layer gives work to the installed applications in the form of Java Class Library. 216.Application developers takes the services of this layer for application development 216.Application layer is the top layer in the stack where your application will get install 216.III. OPENCV IN ANDROIDThe OpenCV library was formally developed and introduced by Intel in 1999 to enforce CPU and GPU exhaustive application 17. The earlier version of OpenCV was written in C27. From the edition 2.0 OpenCV provided both C and C++ interfaces27. In the next edition of 2.2 they had introduces Android port with somewhat sample applications of image processing. Currently it has several optimized methods with the version OpenCV 2.4.927 17.IV. real time video processing methodsTo find and compare t he efficiency of OpenCV and CamTest, each processing method of mobile computer vision was applied and average re determine was calculated 2. The input format of video draw should be in standard form such as RGB space227.The input video frame to RGB conversion is done by following relation 28R = 1.164(Y 16) + 1.596(V 128)G = 1.164(Y 16) 0.813(V 128) 0.391(U 128)B = 1.164(Y 16) + 2.018(U 128) (1)Each pixel of video frame is threshold with a constant number T. If it is greater than T, pixel will be make out 1, otherwise 0. g(x,y) = 1, if f(x,y) T = 0, otherwise (2)Where f(x, y) is the original frame and g(x, y) is the threshold frame. The descriptions of processing methods are shown in Table I.TABLE I. FRAME PRCESSING METHODS AND ITSDESCRIPTIONV. METHODOLOGY offset designed application layout using JAVA and XML. Then, the processing methods and object detection algorithms are written using JAVA and OpenCV. The tools used for designing and schedule are Android SDK 16, Open CV 17 and JAVA SDK.Application file is then installed to the LG Optimus Vu. If there are no errors, then started to measure the result regarding frame processing rate. subsequently all the data had been collected, and the result is analyzed and compared with the theory. The Application flow is shown in Fig.1.0 and Fig.1.1A) System operate of Real Time Video Processing and Object Detection AlgorithmsNoYesNextRealTimeVideoFramezFig. 1.0 Real time video processing flowB) System Flow of Real Time Object Detection AlgorithmsNo Yes Next Real Time VideoFrameFig. 1.1 Real time object detection algorithms flow.VI. EXPERIMENT RESULTSA) Performance of Real Time Video Processing MethodsFor the calculation of processing efficiency of OpenCV and CamTest is calculated by following formula. (7)The unit of FPR is frames bear upon per second i.e. fps. If the value of Frame Processing Rate(FPR) is high for the special processing metohd then theat method is more streamlined. Higher the value of F PR represents the method is more efficient. Table II. Shows real time video processing methods and frames processed per second by CamTest, OpenCV test.TABLE II. REAL TIME VIDEO impact METHODS AND FPS OF CAMTEST AND OPENCV TESTFrame Processing Ratio is as follows,FPR Ratio = (OpenCV FPR CamTest FPR)/OpenCV FPR (8)As from Table II, FPR shows significant differences between OpenCV and CamTest.If there is Positive FPR ratio value e.g N, then OpenCV is 1/N propagation better than CamTest.If there is negatively charged FPR ratio value e.g M,then CamTest is 1/M times better than OpenCV.As shown in Table III, Frame Processing Rate Ratio(average) is 0.64,leads to a conclusion that OpenCV (1/0.64 times) 1.56 times faster and better than CamTest.TABLE III. REAL TIME VIDEO process METHODS AND FPR RATIOFig. 2.0 Frame processing rate using CamTest and OpenCV test for eighter from Decatur image processing methods.B) Performance of Real Time Object Detection AlgorithmsTABLE IV. REAL TIME headi ng DETECTION ALGORITHMSAND THEIR FPSFig. 2.1 Frame Processing Rate for object detection algorithm.As shown in Table IV and Fig. 2.1, FAST algorithm is having the highest fps value and 10 times faster as compare to SIFT and SURF.The minimus fps for real time object recognition should be at least 15 fps and FAST achieves the intimately same thing. So that FAST is having optimum performance in real time scenario fleck executing real time object detection operation.VII. APPLICATIONAs from experimental results shown supra in Table IV, we concluded that FAST algorithm 12 is almost several times faster than other algorithms. To recognize the object in real time video FAST algorithm almost achieves 15 fps. As FAST algorithm extracts the corner features accurately and it requires little time for it. So proposed a Real Time Object realisation system using FAST algorithm is as follows.A) System Flow of Real Time Object realisationAs shown in Fig. 3.0 comment object image is captured by S martphone camera and it is saved to internal storage. FAST corner detector 12 algorithm is applied on the captured image to extract the features. The extracted features should have the same number and location as the viewpoint and corner changes. So the extracted features should be adjusted to the same number and it called as normalization. later on the features are adjusted to the same number, weight is calculated for SVM 15 and BPNN 25 for training the features. afterward that feature database will get created. After the preparation of database object will get recognized in real time video via SVM 15 and BPNN 25. As system recognizes the object it shows the feature count and recognition time on the display of Smartphone.No Input Database YesFig. 3.0 Real Time Object reference FlowA) ResultsThe Real time object recognition system shown above in Fig. 3.0 was developed for LG Optimus Vu and Android platform 4.0.4. The development environment consist of Microsoft Windows 7 with Int el Core i3,2GB RAM,Android SDK,NDK and JAVA SDK.The object used for training was mickle Watch and training time was 102 ms using SVM and 1115 ms using BPNN.The Table V presents the recognition time for object (Hand Watch) using FAST corner detector, SVM and BPNN.TABLE V. RECOGNITION TIME FOR HAND WATCH OBJECTVIII. CONCLUSIONAs per the above experimentation and results, Most of the real time video processing methods executed using OpenCV having high performance with respect to efficiency than the CamTest. OpenCV gives more attention towards the efficinecy than the CamTest.As per the result obtained from the real time object detction application, FAST algorithm achieves high efficiency, almost 15 fps compared to other algorithms.For the futurescope, like to enhance the FAST algorithm in terms of accuracy.The proposed real time object recognition system gives faster and accurate recognition of object(Hand Watch) on the Smartphone using SVM and BPNN. In future would like to introduce m ulti object recognition, location tracking on Smartphone platforms,also like to introduce the concept like GPU and gibe computing with OpenCL.REFERENCES1 Nasser Kehtarnavaz and Mark Gamadia, Real-Time character and Video Processing From research to Reality, Synthesis get ats On Image, Video and Multimedia Processing Lecture 5, 2006.2 Khairul Muzzammil bin Saipullah and Ammar Anuar, Real-Time Video Processing Using innate program on Android Platform, 8th IEEE supranational Colloquium on suggest Processing and its Applications, 2012.3 Kanghun Jeong and Hyeonjoon Moon, Object Detection using FAST Corner demodulator based on Smartphone Platforms, First ACIS/JNU International Conference on figurers, Networks, Systems, and industrial Engineering, 2011.4 Paul Viola, Michael Jones, deep real-time Object Detection, Second International Workshop on Statistical and computational Theories of Vision, July2001.5 L. Zhang and D. 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