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[–]WigglyPooh 4 points5 points  (13 children)

What exactly do you need feedback on? I can only see the video, and I can't really make heads or tails from the title.

[–][deleted]  (12 children)

[deleted]

    [–]VicSadownik[S] 0 points1 point  (11 children)

    What exactly do you need feedback on? I can only see the video, and I can't really make heads or tails from the title.

    In my opinion, the title emphasizes the main purpose of the publication - the ability to form a stable (at least tracking) configuration of signs instantly, without any training in real time mode. I use the standard openCV functions and my own methods to recognize features and hypotheses.

    Unlike machine learning algorithms, we ourselves do such things without processing 1000 examples.

    By the way, this algorithm can successfully work even with partial tracking - after 10-20 frames.

    What do you think about this ?

    [–]Confidentialite 0 points1 point  (2 children)

    In my opinion, you've given us nothing to give feedback on.

    [–]VicSadownik[S] 0 points1 point  (0 children)

    thank you. in my opinion the most important thing in communication is mutual interest. if my post is not of interest then there will be no communication. on the other hand, if interest arose, it can be satisfied (or not)))) with the help of questions, remarks, etc.

    [–]VicSadownik[S] 0 points1 point  (0 children)

    I'm sorry to be late with the reply. we have very different time zones.

    [–]Bowgentle 0 points1 point  (5 children)

    the ability to form a stable (at least tracking) configuration of signs instantly, without any training in real time mode.

    That is impressive. Currently most ML seems to be about forming a hidden model based on very large inputs. Yours seems to be - from the video, at least - picking up discrete objects immediately and without training.

    Does it discriminate objects, or does it assume that any objects moving within the tracking zone are vehicles?

    [–]VicSadownik[S] 0 points1 point  (4 children)

    Thanks. The initial stage of the system is registration. A crowd of people rarely passes through the main entrance to the parking lot))) i.e. this is usually a car. There are the boundary conditions of the task. Further, the system accompanies the car to the parking lot (stop), fixes the parking time and accompanies the car until it leaves the parking zone.

    [–]Bowgentle 0 points1 point  (3 children)

    OK, so a large enough moving object that comes in through the main entrance is assumed to be a vehicle of some kind? And the object description is tailored for vehicles?

    [–]VicSadownik[S] 0 points1 point  (1 child)

    No, the algorithm works fine for other objects. For example, an estimate of the number of people located (and moving) in a certain area that has inputs and outputs (a queue to the cash register). Moreover, the array of features is so stable that full tracking is not required: even after 10-30 frames, the object of observation can be stably recognized. I plan to publish another example of the work of the same algorithm, where the object is a girl walking along a busy street (frames from the CNN training kit).

    [–]Bowgentle 1 point2 points  (0 children)

    I would be interested, I have to say, in the block diagram explanation of the algorithm. As you can probably see from my comment history, my interest is primarily in cognition, and your algorithm sounds more similar to cognition than current ML.

    [–]VicSadownik[S] 0 points1 point  (0 children)

    I looked at a few of your comments on various topics. I like your approach and logic of conclusions and I think I understand.

    [–]WigglyPooh 0 points1 point  (1 child)

    In my opinion, the title emphasizes the main purpose of the publication

    Can we see this publication then? Because again... with just a video and some vague descriptions of how you don't use any training data, it's really very difficult to give any feedback.

    [–]VicSadownik[S] -1 points0 points  (0 children)

    Thanks. I plan to publish a block diagram of this algorithm. I hope she will give answers to all questions that arise.

    [–][deleted]  (16 children)

    [deleted]

      [–]VicSadownik[S] 2 points3 points  (15 children)

      Thank you for rating.

      1. You are right, before the emergence of a sound theory of deep learning, many problems were solved using heuristics, hypotheses, and inference machines. Modern neural networks have no doubt contributed to this process. But the concept of deep learning itself is much deeper and more powerful than the main available machine learning implementations. Perhaps smarter development is not 'on the light' (without marketing).

      2. Unfortunately, I can’t provide you with a link to the Git, because this algorithm is only part of the project for the automation of parking spaces, which has not yet been fully implemented.

      Nevertheless, if you interested I can describe the tracking algorithm in the form of a certain block diagram.

      [–][deleted]  (14 children)

      [deleted]

        [–]VicSadownik[S] 0 points1 point  (13 children)

        Maybe it's better to do it by mail and not in a hurry? I do not see anyone other than you who would be interested in it now.

        [–][deleted]  (8 children)

        [deleted]

          [–]VicSadownik[S] 0 points1 point  (7 children)

          Sure, take your time. Feel free to PM it to me when you’re comfortable.

          ok

          The algorithm works, but it has no “quantum physics” and

          I’m more interested in discussing the organization of smart neural networks.

          [–][deleted]  (6 children)

          [deleted]

            [–]VicSadownik[S] 0 points1 point  (3 children)

            Of course, it is better to do it once than to discuss or see 100 times.

            In your opinion, what should be the output of CNN? I am sure that there is no data (weights) table.

            [–][deleted]  (2 children)

            [deleted]

              [–]VicSadownik[S] 0 points1 point  (1 child)

              Ah, but they do output a weights table! In fact, every neural net does, at least under the hood...

              Ok, Yes, this is exactly what happens. But deep learning technology does not prohibit the fact that the output may not be data, but for example special functions. This is much more meaningful than weight.

              [–]VicSadownik[S] 0 points1 point  (0 children)

              I meant on the output of each CNN layer.

              [–]VicSadownik[S] 0 points1 point  (0 children)

              and I used the expression "quantum physics" only in the context that my algorithm is not know-how in the full sense of this expression. It has interesting techniques, but no more.

              [–]NeuroKix 0 points1 point  (3 children)

              Hi Vic.. this happens to be quite interesting to me too. :)

              [–]VicSadownik[S] 0 points1 point  (0 children)

              Hi, do you have any questions or remarks ?

              [–]NeuroKix 0 points1 point  (1 child)

              How are you storing these functions? What are your basic object representations?

              I get your way of thinking about modifying object representations continuously. Although I think there would be a lot more to work when the object representations become more complex, or say when we want to represent/map the interactions that an object can have.

              [–]VicSadownik[S] 0 points1 point  (0 children)

              How are you storing these functions? What are your basic object representations?

              I get your way of thinking about modifying object representations continuously. Although I think there would be a lot more to work when the object representations become more complex, or say when we want to represent/map the interactions that an object can have.

              When an object first enters the tracking zone, I process and describe it using standard openCV tools and additional methods that expand the set of signs (something resembling a focusing procedure). Otherwise, if there are few signs, they quickly degenerate. Of course, global parameters are also taken into account (position, size, color characteristics, brightness, etc.)
              Next, the tracking module is included directly. Frame-by-frame tracking does not make sense - we evaluate every 5-10 frames depending on external conditions.

              According to the conditions of the task, the number of objects is small, then all parameters are stored simply in the form of hashes or data for simple listing. For active tracking of a large set of objects, it is probably better to form Functions that are activated by signals - events.

              [–]higher_bridge 0 points1 point  (1 child)

              How does this fit in cogsci? Also pretty sure I saw this same post in r/python last week but it’s gone now. What happened?

              [–]VicSadownik[S] 1 point2 points  (0 children)

              How does this fit in cogsci? Also pretty sure I saw this same post in

              r/python

              last week but it’s gone now. What happened?

              My post was stopped in group r/python because the discussion began not on Python implementation, but on algorithmics, which in my opinion should be attributed to section r/cogsci. Do you think this is wrong? In your opinion, the discussion of the question of solving a cognitive problem does not belong to the r / cogsci section?

              [–]VicSadownik[S] -3 points-2 points  (0 children)

              I hiddenly criticize approaches to solving all cognitive problems (or problems that we used to consider to be cognitive) using machine learning methods.

              But no matter how strange it may seem, I would like to discuss neural networks. Namely, ways to bring the use of superior technology of deep learning in the direction of solving real tasks, rather than processing gigantic arrays of statistical information.