Video-based radar for your car

[Gustaf] has been playing around with machine vision for a while and sent in his latest project in on our tip line. It’s a video based car radar system that can detect cars in a camera’s field of vision while cruising down the highway.

Like [Gustaf]‘s previous experiments with machine vision where he got a computer to recognize and count yellow cylinders and green rectangles, the radar build uses ADABoost and the AForge AI/Machine Vision C# framework. [Gustef] used an evolutionary algorithm to detect the presence of a car in a video frame, first by selecting 150 images of cars from a pre-recorded video, and the another 1,850 images were selected by a computer and confirmed as a car by a human eye.

With 2000 images of cars in its database, [Gustaf]‘s machine vision algorithm is able to detect a car in real-time as he drove down a beautiful Swedish highway. In addition to overlaying a rectangle underneath each car in a video frame and an awesome  Terminator-style HUD in the upper right corner, [Gustaf] also a distance display above the hood of his car.

It’s an awesome build that makes us wonder if [Gustef] is building an autonomous car. Even if he’s not, it really makes us want to install a video HUD in our whip, just to see this in action.

Automatic airplane tracking; what radar-systems engineers do for kicks

[G. Eric Rogers] is a radar-systems engineer who just happens to live within sight of the aircraft approach path for the local airport. We wonder if that was one of the criteria when looking for a home? Naturally, he wanted his own home-based system for tracking the airplanes. He ended up repurposing a motorized telescope for this purpose.

The system does not actually use Radar for tracking. Instead, the camera strapped to the telescope is feeding a video experimenter shield. A tracking algorithm analyzes the video and extrapolates vector data. From there, the base unit can be controlled by the Arduino via an RS232 interface.

There are some bugs in the system right now. The Arduino has something of an ADHD problem, losing interesting and going to sleep in the middle of the tracking process. [Eric's] workaround uses the RS232 board to periodically reset the Arduino, but he hopes to squash this bug soon.

Build your own radar system

How we missed this one is anybody’s guess, but one of the presentations at DEFCON last year covers a DIY radar build. [Michael Scarito] talks about the concepts behind radar, and then goes on to show that it’s not too hard or expensive to build a setup of your own. We’ve embedded his 45 minute talk after the break.

The two large pieces of hardware above should look familiar. They’re descendents of a favorite hacking project, the cantenna. The can-based long-range antenna is most popular with WiFi applications, but we’ve seen it used for Bluetooth as well and it’s not surprising to see it here. The rest is a lot of sensing hardware and enough math crammed into the coding to make your ears droop.

If you make it far enough (exactly 39 minutes into the talk) [Michael] shares some links for more information on the build. We think living vicariously is enough for us, but if you manage to build your own setup don’t forget to post a project log!

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A Robot… That Can See Through Walls!

Robots on four wheels are fun on their own merits, but one thing that most lack is the ability to see through walls.  With it’s onboard radar system, this bot is equipped to see objects that a person couldn’t normally detect on the other side of the wall.

Although some of the more “nuts and bolts” details of this build are missing, the robot uses an Ultra-Wideband Radar system called the [D1] Radar System. This system can, according to their documentation, “Avoid false positives caused by vapor, dust, smoke, rain or other small particles.” Apparently this means drywall as well if programmed correctly.

In the video after the break, the robot’s sensor package is programmed to ignore anything within 1.5 meters. This allows the robot to mirror the movement of the apparent shelving unit on the other side. This sensor could certainly have some interesting robotics applications besides imitating a rolling shelf, so we’re excited to see what it will be used for!

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Ultrasonic rangefinder as scanning radar

Ultrasonic rangfinders are a cheap and easy way to gather obstacle avoidance data. When added to a servo motor they form something of a scanning radar for near-proximity objects.

In this implementation, [Rui Cabral] is driving the servo, and collecting data from the sensor using a PIC 18F4520. The servo rotates 180 degreees, taking sensor measurements in increments of nine degrees. If it discovers obstacles, the distance and orientation are recorded. Feedback is displayed on a 20-LED bar graph display which shows a moving LED to track the sensor orientation, with LEDs remaining lit whenever an object is found. Right now the obstacle data is pushed over a serial connection with a PC, but could easily be injected into navigation logic for a robot in order to triangulate a path around the obstruction. You can see [Rui's] project in action after the break.

We looked in on the same concept with a different display technique a couple of years back. That hack used an Arduino and Processing to map sensor data with a traditional green sweep display.

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Autonomous cars already drive the roads among us

Google’s showing off this autonomous car at the TED convention right now, but the hardware has already made automated trips from San Fransisco to Los Angeles. According to the commentary in the video after the break, the scene above shows the car “hauling Prius ass” on a closed course. The car learned this route while being driven by a person and now the vehicle is set to take riders through an aggressively driven loop in the cone-adorned parking ramp. But on the open road you do not need to teach it anything. It has no problem taking a GPS route and following the rules of the road while traveling from one waypoint to another.

The link above doesn’t include hardware information but they did point to a Times article which includes an infographic. The spinning box on the top of the car is 3D-mapping LIDAR with a 200 foot radius. There’s a rotary encoder on one of the wheels for precise movement data, radar sensors on the front and back bumpers, and a rear-view-mirror-mounted camera for image processing. It makes us wonder how the system performs when the car is coated in road-muck? Maybe you just add a dedicated wiper for each sensor.

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Radar gun teardown

[Jeri Ellsworth] is at it again, this time she takes apart a hot wheels speed gun and in the process she does a good job of  explaining how radar can be used to measure speed.  She also demonstrates a way to determine if an object is approaching or receding from the radar gun.

The Doppler shift is one way to remotely measure the speed of an object. It works by measuring the change in frequency of a wave after it strikes an object. Rather than measuring the Doppler shift of the returning wave most radar guns use the phase shift. The reason is that the frequency shift of a relativly slow object (60mph), to a relitivly high frequency signal(10GHz) is small (about 0.893Hz), where the phase shift varies based on the distance of the object.  This is all just a stepping stone in her quest to build a crude TSA body scanner.