We first employed the Viola-Jones toolbox from Matlab to capture the face for each frame, but we found out both the false positives and false negatives are pretty high. Then we found similar problems while applying HOG feature detection for the upper body. However, there is a large overlap between the success rate in terms of frames for those two methods, that is, when face and upper body are detected in one region at the same time, that region is highly likely to contain a person; on the other hand, we understand that false positive are much harder to eliminate in the following denoising process while false negatives can be filled in by predicting the movements of the human body. As a result, we employed the algorithm that detect the face and human body at the same time, and we select the pair of y coordinates from both detections that are within a certain range, (assuming the person is jumping upright), then we simply disregard all the other detections.
Both Viola-Jones and HOG human body detection have adjustable threshold parameters to detect how strictly we recognize an object as a face or human body. We found out that higher threshold tends to produce better results if the background of the input is relatively complicated, and lower threshold tend to produce better results if the background of the input is clear and simple. For now, our algorithm has taken the threshold parameter as an input, we are planning on internalizing it so that the algorithm can automatically set the threshold based on the condition of the image.
2. denoising:
Denoising process involves correcting false positive and false negatives errors introduced by detection. Approaches to reduce false positives errors have been talked about in the previous section. To deal with false negatives, based on the assumption that jumping height changes quadratically with time we used quadratic regression curve to fit the height-time curve. If not all the faces are detected in a frame, we would use points predicted by the regression model as the height of the face in that frame, as illustrated by Figure. 1. This way, we will be able to deduce the relative position of the face in each frame where the algorithms fails to locate the human face. We can also ensure that all the data points are available for the final selection process.
Fitting
Fitting missing data points at frames 4, 8, and 11
3. calibrate with reference to horizon:
The problem might occur that hand and camera shakes cause a person to be high in a frame, but this frame in fact is not where the person is jumping the highest. To capture the point where the person is truly at the highest point of his trajectory, we not only track the height of a person in the image, not also his absolute height, as measured with reference to the horizon. When the program runs, it performs constant background subtraction. If it is detected that background from frame to frame changes above a pre-selected threshold, which indicates that the camera is not fixed properly, the program will use the absolute height instead of the relative height in the image.
4. multiple jumpers:
In the case that more than one person jumps, at this stage we have to ask the user to specify how many people jump at the same time. Assuming that when a person jumps, his/her motion in the horizontal direction is small. Then, based on every person’s initial coordinates, we take face recognized at roughly the same horizontal position as the face that belongs to the same person. In this way, we separate each person’s jumping trajectory and will be able to track individually.
5. selection:
Once the previous steps are achieved, the selection process is easy: the algorithms will selected the index of the frame where the distance between the reference point and face is maximum. Figure. 3 illustrates the selected optimal picture when the program runs on the photo stream shown by Figure. 2.
Input photo stream
Input photo stream
Perfect moment
Best frame selected.
Vii. results:
Judging from our demonstrations during the design process and the presentation session, we conclude that our application is able to fulfill its designed purpose of detecting one person with relatively short runtime （roughly 30 seconds for this prototype) . It is able to catch the frame that is within best 3% of the actual frames. For multiple people detection, the algorithm works relatively after we specify how many people are jumping, and it can catch the frame within best 10% of actual frames.
Viii. future work：
We have successfully constructed this algorithm to capture the perfect moment for a single person. Now we are working to enable this application to capture multiple people jumping at the same time. We are trying to tackle the following difficulties in future development:
The algorithm needs to know how many faces to track and this input metrics is manually given right now. We will need work towards auto detection.
Multiple people might not jump at the same time, thus creating situations where the peaks of each projectile do not occur in the same frame. We are considering algorithms that could find the best possible relative position of faces within each frame.
As specified above, we have to set the threshold of the detection model as an input based on the complexity of the back ground. In the future, we are looking to let the algorithm detecting the complexity of the back ground and decide the threshold on its own.
We have designed our GUI for an IOS application already, and we found out open source packages of both Viola Jones algorithms and HOG feature detection algorithms online, so we are planning on integrating them together as a real smartphone application. 30 seconds are clearly not user-friendly enough, so we are definitely aiming for instant generation of results.
After talking with some audiences during the poster presentation, we are considering the possibility of real-time processing including a level of parallel computing and deep learning techniques for detecting faces more quickly.
Three charges q_{1}=+3\mu C, q_{2}=+6\mu C and q_{3}=+8\mu C are located at (2,0)m (0,0)m and (0,3) coordinates respectively. Find the magnitude and direction acted upon q_{2} by the two other charges.Draw the correct graphical illustration of the problem above showing the direction of all forces.
To solve this problem, we need to first find the net force acting on charge q_{2}. The magnitude of the force exerted by q_{1} on q_{2} is given by F=\frac{kq_{1}q_{2}}{r^{2}} where k is the Coulomb constant, q_{1} and q_{2} are the charges of the particles, and r is the distance between them.
Muhammed
What is the direction and net electric force on q_{1}= 5µC located at (0,4)r due to charges q_{2}=7mu located at (0,0)m and q_{3}=3\mu C located at (4,0)m?
Capacitor is a separation of opposite charges using an insulator of very small dimension between them. Capacitor is used for allowing an AC (alternating current) to pass while a DC (direct current) is blocked.
Gautam
A motor travelling at 72km/m on sighting a stop sign applying the breaks such that under constant deaccelerate in the meters of 50 metres what is the magnitude of the accelerate
velocity can be 72 km/h in question. 72 km/h=20 m/s, v^2=2.a.x , 20^2=2.a.50, a=4 m/s^2.
Mehmet
A boat travels due east at a speed of 40meter per seconds across a river flowing due south at 30meter per seconds. what is the resultant speed of the boat
which has a higher temperature, 1cup of boiling water or 1teapot of boiling water which can transfer more heat 1cup of boiling water or 1 teapot of boiling water explain your . answer
I believe temperature being an intensive property does not change for any amount of boiling water whereas heat being an extensive property changes with amount/size of the system.
Someone
Scratch that
Someone
temperature for any amount of water to boil at ntp is 100⁰C (it is a state function and and intensive property) and it depends both will give same amount of heat because the surface available for heat transfer is greater in case of the kettle as well as the heat stored in it but if you talk.....
Someone
about the amount of heat stored in the system then in that case since the mass of water in the kettle is greater so more energy is required to raise the temperature b/c more molecules of water are present in the kettle
pratica A on solution of hydro chloric acid,B is a solution containing 0.5000 mole ofsodium chlorid per dm³,put A in the burret and titrate 20.00 or 25.00cm³ portion of B using melting orange as the indicator. record the deside of your burret tabulate the burret reading and calculate the average volume of acid used?
No. According to Isac Newtons law. this two bodies maybe you and the wall beside you.
Attracting depends on the mass och each body and distance between them.
Dlovan
Are you really asking if two bodies have to be charged to be influenced by Coulombs Law?
Robert
like charges repel while unlike charges atttact
Raymond
Got questions? Join the online conversation and get instant answers!
