Vehware Project ---

01/31/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc... .

-Discussed the CPT process and printed the forms.

- Attachments A and B are updated for CPT forms

- CPT forms are prepared

- Previous Senior Project codes are prepared to send to Merwyn Jones

-Spoke with Mr. Bill on semester project and he identified a few to-dos to optimize the existing code,also talked about achievements done so far and technologies that are already launched public to get information to lead our ways.

-Decided the future goals talked about algorithms to use and discussed the pros and cons of them. Also the team wants Mr Bill to find help with asking questions about our problems about our code and algorithms.

- Detailed new Gantt chart going to be ready and going to be uploaded here based on selected human and car detection algorithms

-For the this semester we are going to deliver the plan items via the Gantt chart and we will end up with a program that does:

1 Auto detection of cars

2 Notification to driver

3 Danger zones

4 Human detection and Tracking

5 Reduce false alarms

- As a team we make task sharing and start to work on our own goals (like detailed gantt chart, finding and trying human detection algorithms, pedestrian detection with machine learning)

Huseyin is assigned to research the vehicle detection by plates

Ahmet : research pedestrian detection using hog, and application of machine learning for object detection

Okan: study example code of a autonomus RC car

02/07/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

-Ganttchart will be updated upon the decision made on meeting with Mr. Bill

-CPT forms were signed and handed in to Watson office.

-During that time team members did their independent researches

-Histogram oriented gradients method is studied.

-Histogram oriented gradients (HOG) aimed to used to find "gradient" of a pixel. What the gradient information contains whether the pixel is on an edge line, direction of this edge, and how strong is this edge (src)

-Human body has a specific shape and can be easily distinguished among other object by its gradient properties. But in order to do that developer needs a pre-trained HOG features.

This photo is the output of the built-in HOG function of OpenCV. While It seems that method gives accurate results, the amount of time that the method spends is too much for a picture. It needs to be reduced.

-Detecting cars based on their general properties might be good idea for automatic detection because of that focused on plate recognition

-Cars has certain shape of plates only difference between them is their colors

-Detecting vehicles by plates could decrease calculation and make algorithm faster

-Disadvantage of the plate based recognition of the car is when an out ridden car might be out of range in small distance when we compare with the holistic detection of the car

-Started to search on Aforge.net library which include faster algorithm on shape recognition.

-Encountered with significant level of false alarms

02/14/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

- For this week all team members will search how to capture cars from the sides of the vehicle

- Based on that search Mr. Bill and team members will decide on concentration topic

After a meeting we have decided that we will share the task among team member. Okan and Huseyin will work on detecting and tracking vehicles at blindspot of a track.

Ahmet will work on pedestrian detection.

These two will be our main topic for this semester.

02/21/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

Gantt chart needs to be posted here !...

Results of meeting with Mr. Bill ..

Next meeting date with Mr. Bill is 02/22

-At the end of this week, we are expected to have a video shot from the back of camera for the testing purposes.

-HOG will performed on the video stream and the performance of algorithm will be analyzed.

-A method that measures the distace between pedestrian and vehicle will be initiated to implement.

Blind Spot Car Detection

- Mean shift based tracking - camshift

- Maximally stable external region extraction : The algorithm identifies contiguous sets of pixels whose outer boundary pixel intensities are higher (by a given threshold) than the inner boundary pixel intensities. Such regions are said to be maximally stable if they do not change much over a varying amount of intensities.

- lk_track :sparse optical flow demo. Uses goodFeaturesToTrack for track initialization and back-tracking for match verification between frames.\

Pedestrian detection

-A video footage was recording for testing purposes. In this video bunch of purple human were released at the back of the vehicle and the video was recorded while the vehicle backing up. (Thanks to Besket team)

-Opencv Hog pedestrian detection algorithm is performed on the test video.

- After the function is performed it is seen that amount of time to process a frame is about 3 second. And the false alarm rate is too high.

- After the meeting with Mr. Bill on 02/22, it is decided that we use the same method on Linux environment with python language.

02/28/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

- For this week team members build upon last week's codes blind spot area car tracking and pedestrian tracking

- Development team received a laptop Mint OS installed in it. (03/03/2016)

- A list of library will be set up on this machine by the end of this week. (boost, g++, Cmake)

03/06/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

--- For blind spot area car tracking

--- Team members tried to reduce unrelated tracking points, not found a proper way to reduce unrelated tracking points

--- Way of moving points different as we see in the picture below. Team members is going to detect reverse point from the normal car direction and erase them.

--- After removing reverse point we mainly have moving forward points those we need.

--- Team members find a way to detect a car at blind spot area based on tracking point density (after reduction of the false tracking points) a square will be drawn around the certain points

--- As we see in the picture after reducing reverse direction points, the rest are mostly belongs to car's points. Detecting that intense point regions will help to detect car.

03/13/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

- As we mentioned one week before we achieved to eliminate to points which goes reverse direction flows

- Code just shows tails of points for just same direction with the car

- In order to do that we interfered the two dimensional array that holds the tracking points

- When we increase the speed of the video stream we saw that we loss some major points

--- the reason for loosing some major points is that since video stream skips some of the frames when we increased the speed of the speed for better performance.

--- unfortunately increasing video stream speed doesn't helps to increase efficiency

Github user	Language	Method	Required Libraries
R1C1	R1C2	R1C3
R2C1	R2C2	R2C3

03/20/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

03/27/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

04/03/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

04/10/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

04/17/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

04/24/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

05/01/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...

05/08/2016 Weekly activities including accomplishments, problems changes to plan, meeting minutes, etc...