Markov chain Monte Carlo based video tracking algorithm

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

Konushin

2021

In this paper, we consider the problem of people counting in video surveillance. This is an important task in video analysis, because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. The proposed methods are based on object tracking and are able to work on sparse frames, which allows them to work faster and requires minimum computing resources. We use the algorithm from [1] as a baseline, which based on object tracking by head detections. Head tracking in baseline is proved to be more robust and accurate as the heads are less susceptible to occlusions. But this approach has two disadvantages: the height of people is different, which means that people’s heads are in different planes, so the raised signal line doesn’t look so clear, and also because of this, the accuracy of people counting may decrease. In baseline, this problems were solved using head-to-body linear regression, which had to be retrained for each scene, but this complicates the use of the algorithm for practical purposes. In this paper, we propose a new neural network head-to-body regressor, which allows us to solve the mentioned problems at once. Also in this paper, we use a new visual tracking algorithm that allowed us to speed up our solution. In this work, we introduce two methods — distributed modified baseline algorithm with high people counting accuracy and a solution that can run on a single processor core. Our experimental evaluation showed that the proposed modifications are consistent.

Section: Related Workmentioning

confidence: 99%

Practical People Counting Algorithm

Mamedov

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

Konushin

2021

“…In most online algorithms tracks are constructed frame by frame, each frame creates a matrix of the cost of matching new detections and existing tracks, then the problem of matching is solved by a greedy algorithm (searching for the maximum in each row/column) [11,12] or by a Hungarian algorithm [13] [2,3,4]. Sometimes MCMC is used to bind all the detections to tracks [5,14].…”

Section: Related Workmentioning

confidence: 99%

A Distributed Tracking Algorithm for Counting People in Video by Head Detection

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

Geraskin

Mamedov

et al. 2020

We consider the problem of people counting in video surveillance. This is one of the most popular tasks in video analysis because this data can be used for predictive analytics and improvement of customer services, traffic control, etc. Our method is based on the object tracking in video with low framerate. We use the algorithm from [1] as a baseline and propose several modifications that improve the quality of people counting. One of the main modifications is to use a head detector instead of a body detector in the tracking pipeline. Head tracking is proved to be more robust and accurate as the heads are less susceptible to occlusions. To find the intersection of a person with a signal line, we either raise the signal lines to the level of the heads or perform a regression of bodies based on the available head detections. Our experimental evaluation has demonstrated that the modified algorithm surpasses the original in both ac- curacy and computational efficiency, showing a lower counting error on a lower detection frequency.

“…Могут быть рассмотрены связи между соседними обнаружениями [9], могут учитываться несколько последних обнаружений [5] или же на вход подается вся видеопоследовательность целиком, и есть возможность учитывать обнаружения на всех кадрах: предыдущих и последующих. А в некоторых работах предлагают строить траектории из обнаружений с помощью MCMC (Markov chain monte carlo) [12], [14]…”

Section: обзор существующих методовunclassified

Algorithm for Counting Cars in Large-scale Video Surveillance Systems

Shirokov

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 1

Konushin

2020

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.