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Abstract To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network.
Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm.
The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments.
Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time. Introduction Multiple object tracking is a hot topic in the field of computer vision.
Robust tracking of objects is important for many computer vision applications, such as human-computer interaction, video surveillance, intelligent navigation [ 12 ]. Apart from a high performance detection algorithm as an auxiliary, high quality multiobject tracking should also track the algorithm for support, which can address certain types of complex cases, for example, illumination, occlusion, clutter, and so on [ 3 ].
The data association DA method is a favorite for multiobject tracking. The often utilized techniques include the nearest neighbor method [ 4 ], joint probability data association [ 5 ], and methods based on neural networks [ 6 ]. The effect of the DA methods mentioned above is closely related to the detection accuracy in adjacent frames.
These typical approaches are resilient to false negatives and false positives: Recent papers have proposed different approaches to this problem. This method obtains a relatively accurate tracking trajectory but requires a sufficient number of sampling points. This can avoid the NP-completeness.
The accuracy of this method is inversely proportional to the length of the short tracking tracks, the shorter the length, the better the tracking. However, the excessive division increases the computation time, due to which the method cannot track objects for long time.
These approaches, while effective, cannot attain the global optimal solution. However, the two algorithms he proposes have several defects in practice and their complexity is polynomial. Under this framework, Berclaz et al. By relying on the k-shortest paths KSP algorithm for the optimization of the LP problem, their approach reduces the complexity to perform robust multiobject tracking in real time.
Pirsiavash [ 14 ] continues the work of Zhang and uses his method to obtain the global optimal solution with the greedy algorithm for in but only obtains the approximate solutions for inwhere is the unknown optimal number of unique tracks. By contrast, in this paper, we effectively combine the models proposed by Zhang and Berclaz to devise a more efficient framework for the shortest path faster algorithm SPFA.
Not only can the SPFA algorithm directly obtain the global solution, it also shows the advantage of the DP motion model, which enables the algorithm to ignore incomplete trajectories and behave more robustly against this type of noise. Moreover, it is far better with respect to both the worst-case complexity and the run time than the above-mentioned state-of-the-art algorithms.
Our main contributions in this paper are as follows. Compared with the state-of-the-art methods of [ 1314 ], the SPFA algorithm can improve the running time obviously while the multiobject tracking precision and accuracy are not loss.
The rest of this paper is organized as follows. In Section 2we formulate an IP using the min-cost network flow framework and relax it to continuous LP. Section 3 contains our proposed shortest path faster algorithm for the relaxation of the original IP.
We introduce approaches to target localization and long sequence segmentation processing in Section 4. Section 5 contains the experimental results and a complete evaluation metrics. Finally, conclusions are drawn in Section 6.
Network Flow Framework The target motion of multiobjet tracking can be better described using the relationship between the neighborhood locations that use the DP method in a min-cost network flow framework. We define an objective function for multiobject tracking in the same manner as in [ 13 ].
The objective presence of likelihood will be estimated by the marginal posterior probability in every frame, thereby obtaining the potential trajectory of the moving object. Min-Cost Flow Model We formulate the multiobject tracking as a process, where the objective location of each object discretely changes in continuous time.
A directed 3D spatiotemporal group with random variable is used to describe the video sequence. Consider where denotes any location of an object in this spatiotemporal group at timeis the set of all space-time locations in a sequence, and and are the pixel positions of the target in the transverse and longitudinal axes, respectively.Published: Mon, 5 Dec Multiple Objects Tracking Via Collaborative Background Subtraction.
Object tracking system is a group of integrated modern technology working together to achieve certain of purpose like monitoring, tracking moving object such as vehicle.
Regarding these auxiliary objects as the context of the target, the collaborative tracking of these auxiliary objects leads to efficient computation as well as strong verification.
Our extensive experiments have exhibited exciting performance in very challenging real-.
1Department of Computer Science, Kuvempu University, Shimoga, India () have proposed rule based multiple objects tracking system for traffic surveillance using a collaborative background extraction algorithm.
Machine Learning and Applications: An International Journal (MLAIJ) Vol.3, No.4, December Kalman filter and background. In this paper, we proposed a novel method for visible vehicle tracking in traffic video sequence using model based strategy combined with spatial local features.
Our tracking algorithm consists of two components: vehicle detection and vehicle tracking. In the detection step, we subtract the background and obtained candidate foreground objects represented as foreground mask.
Motion-Based Multiple Object Tracking. Detection of moving objects and motion-based tracking are important components of many computer vision applications, including activity recognition, traffic monitoring, and automotive safety. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models.
framework using multiple collaborative cameras for robust and eﬃcient multiple-target tracking in crowded environments with objects and proposed a probabilistic exclusion principle to proposed a sampling-based multiple-target tracking method using background subtraction.
Khan et al.