Grouping Model for People Tracking in Surveillance Camera

Person tracking and analysis from images or videos captured by closed-circuit television (CCTV) play an important role in forensics applications. In this extended abstract, we describe a tracking model by group analysis. This framework improves over state-of-the-art tracking algorithms by leveraging an online learned social grouping behavior model. This group model is practical in real-world applications where group changes (e.g., merge and split) are natural among pedestrians. 1. Motivation Automatic tracking of multiple targets is crucial for many security based applications such as visual surveillance, and anomaly detection. The goal of multi-target tracking is to recover trajectories of all targets while maintaining consistent identity labels. There are many challenges for this problem, such as illumination and appearance variation, occlusion, and sudden change in motion. As great improvement has been achieved in object detection, data association-based tracking (DAT) has become popular recently [4]. In the DAT framework, often a pre-learned detector is applied on each frame to produce detection responses of all targets, and short-term tracking results (i.e., tracklets) are generated by associating responses from consecutive frames that have high probability to contain the same target. These tracklets are further linked to produce long-term tracking results. However, the DAT based tracking methods face problems such as identity switch and track fragmentation still exist in current association based tracking approaches, especially under challenging conditions where appearance or motion of the target changes abruptly and drastically, as shown in Fig. 1. Nevertheless, there is often other useful high level contextual information in the scene which can be effectively used to mitigate the aforementioned shortcomings. For instance, sociologists have found that up to 70% of pedestriFrame 1002 Frame 931 Frame 798 Frame 912 53 Figure 1. Examples in which grouping information is helpful under the challenging conditions for tracking in a video. The same color indicates the same target. ans tend to walk in groups in a crowd, and people in the same group are more likely to have similar motion pattern and be spatially close to each other for better group interaction [3]. Moreover, pedestrians in the crowd often either consciously or unconsciously follow other individuals with similar destination to facilitate navigation [2]. It is also observed in many real world surveillance videos that if two people are walking together at certain time then it is very likely that these two people will still walk together after a short time period. 2. Approach Based on the above observations, we propose an elementary grouping model with non-linear motion context to compensate the errors caused by using basic appearance model and linear motion model. A grouping graph is constructed based on input tracklets with high confidence, where each node represents a pair of tracklets that form an elementary group (a group of two targets) and each edge indicates that the connected two nodes (two elementary groups) have at least one target in common. The group trajectories of any Tracklets Generation Detection Responses T1 T2