Online Multi-target Visual Tracking using a HISP Filter

Abstract

We propose a new multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like multiple hypothesis tracking (MHT) and point-process-based approaches like probability hypothesis density (PHD) filter, and has a linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. In addition, we alleviate the problem of two or more targets having identical label taking into account the weight propagated with each confirmed hypothesis. Finally, we carry out extensive experiments on Multiple Object Tracking 2016 (MOT16) benchmark dataset and find out that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.

Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning

“…Benchmark Evaluations: We make comprehensive performance evaluations of our proposed online tracker, HISP-DAL, and compare it against state-of-the-art online as well as offline tracking algorithms such as MHT-DAM [10], MHT-bLSTM [32], IOU17 [49], DP-NMS [6], SMOT [50], CEM [5], JPDA-m [51], EAMTT [1], GM-PHD-HDA [2], GMPHD-KCF [52], GM-PHD [53]), GM-PHD-N1T [27], GM-PHD-DAL [3], HISP-T [22], JCmin-MOT [54], SAS-MOT17 [55], FPSN [56], OTCD-1 [57] and SORT17 [58].…”

HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

“…We restrict our evaluation to only those methods that are published in peer reviewed journals and conferences. We evaluate 37 different trackers (Brasó and Leal-Taixé 2020;Wang et al 2019;Bergmann et al 2019;Sheng et al 2018;Maksai and Fua 2019;Yoon et al 2020;Zhu et al 2018;Keuper et al 2018;Chen et al 2017Chen et al , 2019Xu et al 2019;Henschel et al 2018Henschel et al , 2019Long et al 2018;Kim et al 2015Kim et al , 2018Yoon et al 2018;Fu et al 2018Fu et al , 2019Chu and Ling 2019;Liu et al 2019;Song et al 2019;Karunasekera et al 2019;Babaee et al 2019;Cavallaro 2016, 2019;Bewley et al 2016;Bochinski et al 2017;Baisa 2018;Song and Jeon 2016;Baisa 2019;Eiselein et al 2012;Kutschbach et al 2017; Baisa and Wallace 2019) on MOT17 (Milan et al 2016). This includes all of the trackers for which the relevant bibliographic information was available when this analysis was performed on the 1st April 2020.…”

MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

“…The aforementioned methods only learn general appearance embedding vectors for object detection and do not adapt the tracking target appearance models online. Further performance is gained by methods that perform such adaptation online [Chu et al, 2017, Kim et al, 2015, 2018. MHT bLSTM [Kim et al, 2018] replaces the multi-output regularized least-squares learning framework of MHT DAM [Kim et al, 2015] with a bi-linear LSTM and adapts both the appearance model as well as the convolutional filters in an online fashion.…”