### Table 1: Performance of the object tracker, comparing the number of vehicles and pedestrians tracked (columns) with the actual events as defined by an operator (rows)

### Table 1. Results of clustering trajectories into vehicles and pedestrians. I: compare average observation size along the trajectory and use spectral clustering; II: compare more observa- tion features, (size, speed, size variation, aspect ratio and percentage occupancy of silhou- ette), also averaged along the trajectory; III: size similarity defined in (2)(3)(4) without considering comparison confidence; IV: compare trajectory distance in space as define in (1); V: combine size similarity and comparison confidence as described in Section 3.2 and 4.

2006

"... In PAGE 9: .... Wang, K. Tieu, and E. Grimson 5.3 Experiments In Table1 , we report the results of clustering trajectories into vehicles and pedestrians using different clustering methods and similarity measures. There are two data sets from the two scenes shown in Figure 1.... ..."

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### Table 1. Possible Worlds and a Probability Distribution for the Simple Boolean Collision Model

2003

"... In PAGE 11: ... Since all variables are Boolean, the relationship between y, x and v can be tabulated as in Table 1. In Table1 each assignment of values to v and x determines a possible way the vehicle/pedestrian encounter could have occurred. The rows of such tables have been variously referred to as quot;states of affairs, quot; quot;scenarios, quot; or quot;system states, quot; but a long-running practice in philosophical logic (e.... In PAGE 11: ... For example, suppose one is interested in whether or not the vehicle was speeding, but the only evidence is that the accident occurred (y=1). Table1 shows that the condition y=1 eliminates world 3 as a possibility, but of the remaining three worlds at least one has v=0 and one has v=1, so the best that can be said is that it is possible, but not necessary, that the vehicle was speeding. On the other hand, suppose that a reliable witness reported that the initial distance was quot;long quot; (x=1) when the pedestrian entered the road.... In PAGE 11: ... Uncertainty can be modeled by placing a probability distribution on the set of possible worlds, so that the probability attached to a statement is simply the probability assigned to the set of possible worlds where that statement is true. For example, suppose that each of the possible worlds in Table1 is regarded as a priori equally probable, so that each has a prior probability of 1/4. One then... In PAGE 12: ... For instance, suppose the actual world is world 4 (v=1, x=1) and world 3 (v=0, x=1) is taken to be the world closest to world 4, but having v=0. Letting yv=0=0 stand for the counterfactual claim that had v been 0, y would have been 0, Table1 shows that since y=0 is true in the possible world 3, yv=0=0 should be taken as true in the actual world 4. On the other hand, yv=0=0 should not be taken as true in world 2 (v=1, x=0) if world 1 (v=0,x=0) is taken as the closest with v=0.... ..."

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### Table 1: Selected previous strategies for state aggregation

2006

"... In PAGE 3: ... However, if such abstract MDPs are used to learn a policy for the larger MDP, they may not yield optimal policies [14, 18] and may even prevent some algorithms from converging [12]. A summary of the properties of the aforemen- tioned work is presented in Table1 . The table or- ders the algorithms roughly from strictest to coars- est abstractions.... ..."

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### Table 4. Chemical species MDP resolution for experiments

2007

"... In PAGE 12: ...he process. Details can be found in [22]. 5 Experimental Results In this section we analyze the safety probability for the SHS sugar cataract model presented in Section 3. The chemical concentration ranges used are pre- sented in Table 2, and the resolution of each range is presented in Table4 . We chose the resolution parameters to be similar to the resolution that measurement equipment can achieve in actual experiments.... ..."

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### Table 5: Performance summary - SDN, MDP, MDN models

"... In PAGE 16: ... The summary performance of the other three models on the same data sets is reflected in Tables 5 and 6. Table5 shows the average and boundary optimality gaps for SD model without percentages (SDN) and MD model with (MDP) and without (MDN) percentages. Table 6 shows the average running time for the models.... ..."

### Table 6: Running time (sec) summary - SDN, MDP, MDN models

"... In PAGE 16: ... Table 5 shows the average and boundary optimality gaps for SD model without percentages (SDN) and MD model with (MDP) and without (MDN) percentages. Table6 shows the average running time for the models. Generally the problems become harder to solve as problem size grows.... ..."

### Table 1: Mutual information (M.I.) between object features and labels, measured in bits (max. possible score = 1.0)

2004

"... In PAGE 5: ... Equal numbers of vehicle and pedestrian images from each scene were used. MI scores for our chosen features in the two cases are shown in Table1 . We are mainly interested in comparing relative scores across features and scenes.... ..."

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### Table 2.11: Performance summary - SDN, MDP, MDN models

2002

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