thanks in advance.
I am new to the multiple object tracking field. So, I have been working on this for a couple of days. I have developed my first version of a single object tracker using an extended Kalman filter. I am estimating position, velocity by assuming a constant acceleration model. Now my question is how can I convert the existing model for multiple objects tracking. The main problem is I am using radar data. So, I am not able to get the references for developing the tracker. So, One good example or steps to achieve can help me in understanding the concept.
The answer to this question depends on a lot of things. For example, how much control and knowledge do you have over the whole system? If you know how many targets you need to track you can add all of them to the Kalman Filter state and for every measurement you perform data association to find out to which object a given measurement belongs. An easy association metric would be nearest neighbor.
If you don't know how many targets there will be you will want to implement a track management where each target you are tracking represents a track and you can model birth and death probabilities of targets.
Multi Target Tracking is a vast field and if you want to have an in-depth mathematical introduction I would recommend the 2015 survey paper "Multitarget Tracking" by Ba-Ngu Vo et al. You should be able to find a preprint pdf online.
If you are looking more for a lightweight tutorial I would assume it should be possible to find some tutorial or example code online where to start. As mentioned in the first paragraph, nearest neighbor association for a fixed amount of objects might be a good first step.
Related
I'm looking at finding people with similar purchasing behaviors with a given person or group as a starting point for a market research problem.
I'm going to use vectors and represent every person and their habits as a vector and then compare these vectors to return the base person or group. I'd probably use Faiss. I believe KNN can be used too.
But I'm looking to see is if I can use other methods such as clustering methods like k-means clustering for such a question, and with the presence of a given person or group as the base. I thought the only way clustering algs would work is to first cluster the data, then return the group that the 'base person or group' falls into. However, this would be costly and probably not very accurate. But potentially this technique can be used to reduce the search space.
So, do you know of any other ways? (non-Machine Learning or Information Retrieval methods would be welcomed too :) )
I have been working with Anylogic for about 6 months now and my goal is to model a generic energy supply chain for an energy demand (e.g. storm and heat for a house). As a result I want to evaluate how suitable the components in the energy supply chain are to meet the energy demand.
My idea would be to model the components (Ex. PV->Battery Storage->House) as agents. I would have modeled the energy flow in the agents with SD and individual events of the components (e.g. charging and discharging at the battery) via state diagrams.
Currently I have two problems:
Which possibilities are there to create a variable interconnection of my components (agents). For example, if I do not want to evaluate the scenario PV->Battery Storage->House, but PV->Electrolysis->Tank->Fuel Cell->House. My current approach would be to visually connect the agents with ports and connectors and then pass input and output variables for DS calculation via set and get functions. Are there other possibilities, e.g. to realize such a connection via an input Excel? I have seen a similar solution in the video: "How to Build a True Digital Twin with Self-Configuring Models Using the Material Handling Library" by Benjamin Schumann, but I am not sure if this approach can be applied to SD.
To evaluate the energy supply chain, I would like to add information to the energy flow, for example the type (electricity, heat), generation price (depending on which components the energy flow went through) and others. Is there a way to add this information to a flow in SD? My current approach would be to model the energy flow as an agent population with appropriate parameters and variables. Then agents could die when energy is consumed or converted from electricity to heat type. However, I don't know if this fits with the SD modeling of the energy flow.
Maybe you can help me with my problems? I would basically be interested in the opinion of more experienced Anylogic users if my approaches would be feasible or if there are other or easier approaches. If you know of any tutorial videos or example models that address similar problems, I would also be happy to learn from them.
Best
Christoph
Sounds like what you need is a model that combine agent-based and system dynamics approaches with Agents populating the stocks (in your case energy that then gets converted into heat) depending on their connection. There is an example of AB-SD combination model in 'Example' models and I also found one on cloud.anylogic.com, although it is from a different domain.
Perhaps if you can put together a simple example and share then I'll be able to provide more help.
http://python.dronekit.io/develop/sitl_setup.html
there's a. mentioning that SITL is there for only a few pre-built vehicles. suppose i am designing a completely new model of flying vehicle, can i still simulate that?
Thanks a lot:) My drone design just has another set of mathematical expressions for its roll, pitch and yaw control. So, technically I need changes only in the AP_motors.cpp file (here the commands are converted into motor PWM values) only right? or is it more than that? Please guide me.
You can actually, but at this time there is no detailed documentation to do so. At the moment the best bet is by using RealFlight 8 to design the vehicle (and also to simulate the physics) and then use SITL to control it. You can check how to connect RealFlight to SITL at here http://ardupilot.org/dev/docs/sitl-with-realflight.html. There is also a discussion about building a custom model for RealFlight 8 with SITL at here https://discuss.ardupilot.org/t/building-realflight8-models/23106/34.
I am taking part in a programming competition where the objective is writing a bot that can play a specific game.
The objective of the game is to earn a certain amount of points. You control multiple airships, that you move around, capture islands and navigate drones that carry treasure. You play against one opponent, turns happen simultaneously, and there is a time limit. You can move multiple ships and drones in one turn. You can program your bot in Python, Java or C#.
The exact details don‘t matter, just that each ship has around 15 options each turn (moving and shooting) and overall you have around 10000 different options for each turn (different configurations of airship movements and shooting)
Up until now I approached this competition naively, and haven‘t done anything exceptionally clever (for example, if near enemy, shoot). I have read about minimax algorithms, and I would really like to apply it here (or something similar), you can assume that I can tell the value of a state. My problem is the mass of options for each turn - which create an enourmous branching factor that doesnt let me get very deep.
Question 1: Is there a better, applicable approach to this problem? Perhaps deep-learning or something similar?
Question 2: Is there a way to minimize the branching factor? I`ve read about alpha-beta and similar algorithms, but nothing seems to do the job.
Any help would be much appreciated
The minimax algorithm seems to be natural for these kinds of problems. At first, the game will be modelled in a abstract way and then a solver is used to find the path from current situation to a gamestate which maximize the amount of points. A similar approach to minimax is GOAP, which was implemented in the 1970'er for Shakey the robot under the name STRIPS. But, GOAP and minimax has two problems: first, a abstract model of the game is needed (perhaps in PDDL or in Game Description Language) and second the state-space is to big.
An better alternative to planning is to use a Behavior Tree. Thats a static program which describes the behavior of an agent. No solver is needed and no complete modelling of the game is needed. Instead, a bottom up approach is used with multiple edit-compile-run iterations for finding the optimal behavior tree (Test-driven-development). To implement such programming approach a so called "reactive planner" has to be implemented first which is another word for a realtime scheduler. Thats a module whichs maps a behavior tree onto a gantt-chart for executing an action at a specific moment in time. As introduction, the unity3d Engine is a good starting point, which has a full behaviortree implementation out-of-the-box.
I would like to know if there are some libraries/algorithms/techniques that help to extract the user context (walking/standing) from accelerometer data (extracted from any smartphone)?
For example, I would collect accelerometer data every 5 seconds for a definite period of time and then identify the user context (ex. for the first 5 minutes, the user was walking, then the user was standing for a minute, and then he continued walking for another 3 minutes).
Thank you very much in advance :)
Check new activity recognization apis
http://developer.android.com/google/play-services/location.html
its still a research topic,please look at this paper which discuss the algorithm
http://www.enggjournals.com/ijcse/doc/IJCSE12-04-05-266.pdf
I don't know of any such library.
It is a very time consuming task to write such a library. Basically, you would build a database of "user context" that you wish to recognize.
Then you collect data and compare it to those in the database. As for how to compare, see Store orientation to an array - and compare, the same holds for accelerometer.
Walking/running data is analogous to heart-rate data in a lot of ways. In terms of getting the noise filtered and getting smooth peaks, look into noise filtering and peak detection algorithms. The following is used to obtain heart-rate information for heart patients, it should be a good starting point : http://www.docstoc.com/docs/22491202/Pan-Tompkins-algorithm-algorithm-to-detect-QRS-complex-in-ECG
Think about how you want to filter out the noise and detect peaks; the filters will obviously depend on the raw data you gather, but it's good to have a general idea of what kind of filtering you'd want to do on your data. Think about what needs to be done once you have filtered data. In your case, think about how you would go about designing an algorithm to find out when the data indicates activity (like walking, running,etc.), and when it shows the user being stationary. This is a fairly challenging problem to solve, once you consider the dynamics of the device itself (how it's positioned when the user is walking/running), and the fact that there are very few (if not no) benchmarked algos that do this with raw smartphone data.
Start with determining the appropriate algorithms, and then tackle the complexities (mentioned above) one by one.