Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers. A good similarity score is expected to reflect multiple cues, e.g. appearance, location, and topology, over a long period of time. However, these cues are heterogeneous, making them hard to be combined in a unified network. As a result, existing methods usually encode them in separate networks or require a complex training approach. In this paper, we present a unified framework for similarity measurement which could simultaneously encode various cues and perform reasoning across both spatial and temporal domains. We also study the feature representation of a tracklet-object pair in depth, showing a proper design of the pair features can well empower the trackers. The resulting approach is named spatial-temporal relation networks (STRN). It runs in a feed-forward way and can be trained in an end-to-end manner. The state-of-the-art accuracy was achieved on all of the MOT15-17 benchmarks using public detection and online settings.
The topological information is also crucial for measuring similarity @cite_54 . However, leveraging such non-grid topology of multiple objects is challenging. Only a few works successfully encode the topological information, e.g. the occupancy grid in @cite_54 . However, this occupancy grid only counts the distribution of objects, without differentiating individual objects. In this paper, we utilize relation networks to encode the topological information for making the individual object differentiable and identifiable.
@cite_54
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To enable safe motion planning in a dynamic environment, it is vital to anticipate and predict object movements. In practice, however, an accurate object identification among multiple moving objects is extremely challenging, making it infeasible to accurately track and predict individual objects. Furthermore, even for a single object, its appearance can vary significantly due to external effects, such as occlusions, varying perspectives, or illumination changes. In this paper, we propose a novel recurrent network architecture called a recurrent flow network that can infer the velocity of each cell and the probability of future occupancy from a sequence of occupancy grids which we refer to as an occupancy flow. The parameters of the recurrent flow network are optimized using Bayesian optimization. The proposed method outperforms three baseline optical flow methods, Lucas-Kanade, Lucas-Kanade with Tikhonov regularization, and HornSchunck methods, and a Bayesian occupancy grid filter in terms of both prediction accuracy and robustness to noise. Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. Furthermore, when moving in a real dynamic world, traditional occupancy grid mapping is required not only with the ability to detect occupied areas, but also with the capability to understand dynamic environments. The paper addresses this issue by presenting a stereo-vision-based framework to create a dynamic occupancy grid map, which is applied in an intelligent vehicle driving in an urban scenario. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The proposed framework consists of two components. The first is motion estimation for the moving vehicle itself and independent moving objects. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. This is very practical in real applications. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments. Occupancy grid mapping approaches, especially those that additionally estimate the dynamics, enable a robust and consistent modeling of the local environment in a cell-level representation. But a scene understanding of surrounding traffic participants requires a generalized object-level representation. This work presents an object tracking approach based on dynamic occupancy grids. The association of occupied grid cells with existing object tracks is solved individually on the cell-level without clustering or forming object hypotheses. New object tracks are extracted using a clustering strategy and a velocity variance analysis of neighboring occupied cells to reduce false positives. In order to improve the estimates of the position and size, an object boundary extraction is presented that takes the surrounding free space of the selected box representation into account. Experimental results with real sensor data show the effectiveness of the proposed object tracking approach in challenging urban scenarios with dense traffic. In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of @math by recovering the occluded missing regions. The key idea is to combine the generative capabilities of 3D encoder-decoder and the conditional adversarial networks framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.